Systems and compositions for diagnosing Barrett&#39;s esophagus and methods of using the same

ABSTRACT

The invention provides a system, composition, and methods of using the systems and compositions for the analysis of a sample from a subject to accurately diagnose, prognose, or classify the subject with certain grades of or susceptibility to Barrett&#39;s esophagus. In some embodiments, the system of the present invention comprises a means of detecting and/or quantifying morphological features, the expression of protein, or the expression of nucleic acids in a plurality of cells and correlating that data with a subject&#39;s medical history to predict clinical outcome, treatment plans, preventive medicine plans, or effective therapies. In some embodiments, the invention relates to a method of classifying and compiling data taken from a cell sample from a subject analyzing the data, and converting the data from the system into a score by which a pathologist may calculate the likelihood that the subject develops cancer.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/005,409, filed Nov. 27, 2013, now U.S. Pat. No. 10,018,631, which isa U.S. national stage filing under 35 U.S.C. § 371 of InternationalApplication No. PCT/US2012/029198 filed on Mar. 15, 2012 which claimsthe benefit of U.S. Provisional Application No. 61/453,929, filed onMar. 17, 2011.

The entirety of each is incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to a system, composition, and series of methods ofusing the systems and compositions for the analysis of a cell samplefrom a subject to accurately diagnose, prognose, or classify the subjectwith certain grades of or susceptibility to Barrett's esophagus (BE) orcancer of the esophagus. In some embodiments, the system of the presentinvention comprises a means of detecting and/or quantifyingmorphological features, the expression of protein, or the expression ofnucleic acids in a plurality of cells and correlating that data with asubject's medical history to predict clinical outcome, treatment plans,preventive medicine plans, or effective therapies.

BACKGROUND OF THE INVENTION

Barrett's esophagus results from Gastroesophageal Reflux Disease (GERD)and affects approximately 3 million patients in the United States, with86,000 new cases being diagnosed each year. Aldulaimi et al., Eur JGastroenterol Hepatol. 2005 September; 17(9):943-50, discloses that adiagnosis of Barrett's esophagus predisposes patients to developesophageal adenocarcinoma with a risk calculated as 30-125 times more ascompared to patients without a diagnosis. Esophageal adenocarcinomadevelops in a defined sequence of changes from benign, to low gradedysplasia, to high-grade dysplasia, and to malignant cancer.

Patients with Barrett's esophagus are frequently screened (every 3months to every 3 years depending on stage of disease) by endoscopy andbiopsies are taken for histopathology. Biopsies are analyzed by manualmicroscopy analysis with traditional Hematoxylin and Eosin-staining oftissue sections. Diagnosis of Barrett's esophagus is based onestablished histologic criteria and a minimal set of biomarkers measuredsingly to detect abnormalities.

The screening process has many limitations. For instance, diagnosis ofBarrett's esophagus can be characterized in different stages such aslow-grade dysplasia, high-grade dysplasia, and reactive atypia. These“stages” of BE share histological features and are difficult todistinguish using current H&E-based analysis. Frequently, the diagnosisresults in “indeterminate/indefinite,” misdiagnosis, delayed diagnosisor inappropriate treatment. Furthermore, the current form of histologyanalysis is insufficient to diagnose the various stages of the BEdisease accurately and to predict progression to higher disease stages.

There is a need for pathology informatics/descriptive features of BE tointegrate biomarker data, morphological data around a tissue sample, andclinical data into decision-making indices. There is also a need formore accurate diagnostic, prognostic and predictive testing to guideclinical management and prevention of malignant forms ofgastrointestinal cancer. There is a need for increased surveillance andstratification BE staging and prediction of effective treatments orprevention of malignant cancer. The invention relates to a system, anapparatus, a composition, a device and method of using the same toextract specific biomarker information from cell samples to improve theaccuracy of diagnosis, to enable predictions of disease progression andcancer development, to predict responsiveness to therapeuticinterventions, and to improve management of BE or any cancer derivedfrom tissue diagnosed as BE.

SUMMARY OF THE INVENTION

In some embodiments, the invention relates to a composition comprising:(a) a cell sample; (b) a plurality of probes and/or stains that bind tobiomarkers of the cell sample; (c) one or more optical scanners thatgenerates digital imaging data about the presence, absence, location,quantity, and/or intensity of at least one probe or stain that binds abiomarker of the cell sample; (d) one or more data processors that,either individually or collectively: (i) receives the digital image datafrom the optical scanner and, optionally, transmutes said digitalimaging data into a digital imaging signal; and (ii) analyzes thedigital image data to identify, measure, or quantify one or moredescriptive features from the plurality of probes and/or stains; and(iii) converts the one or more descriptive features into a score,wherein (iii) optionally comprises integrating stored data about asubject or group of subjects to convert the one or more descriptivefeatures into a score; (e) one or more monitors that comprises a screenand that receives a component of the digital images, or, optionally,receives the digital imaging signal from the data processor and projectsa digitally addressable image onto its screen; and (f) one or more datastorage units; wherein the one or more optical scanners, the one or moredata processors, the one or more monitors, and the one or more datastorage units are in digital communication with each other by a means totransmit digital data.

In some embodiments, the invention relates to a system or apparatuscomprising: (a) a cell sample; (b) a plurality of probes and/or stainsthat bind to biomarkers of the cell sample; (c) one or more opticalscanners that generates digital imaging data about the presence,absence, location, quantity, and/or intensity of at least one probe orstain that binds a biomarker of the cell sample; (d) one or more dataprocessors, each in operable communication with at least one opticalscanner, that, either individually or collectively: (i) receives thedigital image data from the optical scanner and, optionally, transmutessaid digital imaging data into a digital imaging signal; and (ii)analyzes the digital image data to identify, measure, or quantify one ormore descriptive features from the plurality of probes and/or stains;and (iii) converts the one or more descriptive features into a score,wherein (iii) optionally comprises integrating stored data about asubject or group of subjects to convert the one or more descriptivefeatures into a score; (e) one or more monitors, each in operablecommunication with at least one data processor, that comprises a screenand that receives a component of the digital images, or, optionally,receives the digital imaging signal from the data processor and projectsa digitally addressable image onto its screen; and (f) one or more datastorage units, each in operable communication with at least oneprocessor.

In some embodiments, the invention relates to a system or apparatuscomprising: one or more data processors, each in operable communicationwith at least one optical scanner, that, either individually orcollectively: (i) receives the digital image data from the opticalscanner and, optionally, transmutes said digital imaging data into adigital imaging signal; and (ii) analyzes the digital image data toidentify, measure, or quantify one or more descriptive features from theplurality of probes and/or stains; and (iii) converts the one or moredescriptive features into a score, wherein (iii) optionally comprisesintegrating stored data about a subject or group of subjects to convertthe one or more descriptive features into the score or scores.

In some embodiments, the invention relates to a system or apparatuscomprising: one or more data processors, each in operable communicationwith at least one optical scanner, that, either individually orcollectively: (i) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (ii) converts the one or more descriptivefeatures into a score, wherein (ii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into the score or scores.

In some embodiments, the invention relates to a system comprising: (a) acell sample; (b) a plurality of probes and/or stains that bind tobiomarkers of the cell sample; (c) one or more optical scanners thatgenerates digital imaging data about the presence, absence, location,quantity, and/or intensity of at least one probe or stain that binds abiomarker of the cell sample; (d) one or more data processors that,either individually or collectively: (i) receives the digital image datafrom the optical scanner and, optionally, transmutes said digitalimaging data into a digital imaging signal; and (ii) analyzes thedigital image data to identify, measure, or quantify one or moredescriptive features from the plurality of probes and/or stains; and(iii) converts the one or more descriptive features into a score,wherein (iii) optionally comprises integrating stored data about asubject or group of subjects to convert the one or more descriptivefeatures into a score; (e) one or more monitors that comprises a screenand that receives a component of the digital images, or, optionally,receives the digital imaging signal from the data processor and projectsa digitally addressable image onto its screen; and (f) one or more datastorage units; wherein the one or more optical scanners, the one or moredata processors, the one or more monitors, and the one or more datastorage units are in digital communication with each other by a means totransmit digital data.

In some embodiments, the invention relates to a system comprising: (a) acell sample; (b) a plurality of probes and/or stains that bind tobiomarkers of the cell sample; (c) one or more optical scanners thatgenerates digital imaging data about the presence, absence, location,quantity, and/or intensity of at least one probe or stain that binds abiomarker of the cell sample; (d) one or more data processors that,either individually or collectively: (i) receives the digital image datafrom the optical scanner and, optionally, transmutes said digitalimaging data into a digital imaging signal; and (ii) analyzes thedigital image data to identify, measure, or quantify one or moredescriptive features from the plurality of probes and/or stains; and(iii) converts the one or more descriptive features into a score,wherein (iii) optionally comprises integrating stored data about asubject or group of subjects to convert the one or more descriptivefeatures into a score; (e) one or more monitors that comprises a screenand that receives a component of the digital images, or, optionally,receives the digital imaging signal from the data processor and projectsa digitally addressable image onto its screen; and (f) one or more datastorage units; wherein the one or more optical scanners, the one or moredata processors, the one or more monitors, and the one or more datastorage units are in digital communication with each other by a means totransmit digital data; and wherein the cell sample is taken from asubject identified as having or suspected of having Barrett's esophagusor esophageal cancer. In some embodiments, the cell sample comprises atissue from a brushing, biopsy, or surgical resection of a subject.

In some embodiments, the descriptive features are at least one or acombination of features chosen from: the presence or absence of one ormore biomarkers, the localization of a biomarker within the cell sample,the spatial relationship between the location of biomarker and itsposition in or among the cell sample or subcellular compartments withina cell sample, the quantity and/or intensity of fluorescence of a boundprobe, the quantity and/or intensity of a stain in a cell sample, thepresence or absence of morphological features of cells within theplurality of cells, the size or location of morphological features ofcells within the plurality of cells, the copy number of a probe bound toa biomarker of at least one cell from the plurality of cells

In some embodiments, the cell sample comprises a plurality of cellsand/or biomaterials. In some embodiments, the cell sample comprisesesophageal cells. In some embodiments, the system comprises a cellsample from a subject suspected of or was previously diagnosed withhaving a disorder of the gastrointestinal tract. In some embodiments,the cell sample is room temperature or frozen. In some embodiments, thecell sample is freshly obtained, formalin fixed, alcohol-fixed and/orparaffin embedded.

In some embodiments, the composition or system comprises an opticalscanner, wherein the optical scanner utilizes bright field and/orfluorescence microscopy. In some embodiments, the system measures thelocalization, position, absence, presence, quantity, intensity or copynumber of more than one biomarker. In some embodiments, the compositionor system comprises an optical scanner and cell sample, wherein thesystem simultaneously measures the localization, position, absence,presence, quantity, intensity or copy number of one or more probes orstains bound or intercalated to a cell and/or biomaterial.

In some embodiments, the invention relates to a composition or a systemcomprising: (a) a cell sample; (b) a plurality of probes and/or stainsthat bind or intercalate to biomarkers of the cell sample; (c) one ormore optical scanners that generates digital imaging data about thepresence, absence, location, quantity, and/or intensity of at least oneprobe or stain that binds a biomarker of the cell sample; (d) one ormore data processors that, either individually or collectively: (i)receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (iii) converts the one or more descriptivefeatures into a score, wherein (iii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into a score; (e) one or more monitors thatcomprises a screen and that receives a component of the digital images,or, optionally, receives the digital imaging signal from the dataprocessor and projects a digitally addressable image onto its screen;and (f) one or more data storage units; wherein the one or more opticalscanners, the one or more data processors, the one or more monitors, andthe one or more data storage units are in digital communication witheach other by a means to transmit digital data; wherein the data storageunits comprise stored data that comprises clinical history of a subjector group of subjects. In some embodiments, the subject or group ofsubject is suspected of having or has been diagnosed with agastrointestinal tract disorder. In some embodiments, the subject orgroup of subjects is suspected of having or has been diagnosed withBarrett's esophagus. In some embodiments, the subject or group ofsubjects is suspected of having or has been diagnosed with Barrett'sesophagus, Barrett's esophagus with high-grade dysplasia, Barrett'sesophagus with low-grade dysplasia, Barrett's esophagus with reactiveatypia, Barrett's esophagus indefinite for dysplasia or indeterminateBarrett's esophagus. In some embodiments, the subject or group ofsubjects has been misdiagnosed with a stage of Barrett's esophagus.

In some embodiments, the composition or system comprises one or moredata storage units, wherein the one or more data storage units is indigital communication with the one or more optical scanners, one or moremonitors, one or more data processors from a remote location.

In some embodiments, the composition or system comprises a microscope.In some embodiments, the plurality of probes comprises at least twoprobes that comprise a fluorescent tag.

In some embodiments, the invention relates to a composition or systemcomprising: (a) a cell sample; (b) a plurality of probes and/or stainsthat bind or intercalate to biomarkers of the cell sample; (c) one ormore optical scanners that generates digital imaging data about thepresence, absence, location, quantity, and/or intensity of at least oneprobe or stain that binds a biomarker of the cell sample; (d) one ormore data processors that, either individually or collectively: (i)receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (iii) converts the one or more descriptivefeatures into a score, wherein (iii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into a score; (e) one or more monitors thatcomprises a screen and that receives a component of the digital images,or, optionally, receives the digital imaging signal from the dataprocessor and projects a digitally addressable image onto its screen;and (f) one or more data storage units; wherein the one or more opticalscanners, the one or more data processors, the one or more monitors, andthe one or more data storage units are in digital communication witheach other by a means to transmit digital data; wherein the systemidentifies the location, position, absence, presence, quantity orintensity of fluorescence of at least two fluorescent probessimultaneously. In some embodiments the biomarkers are chosen from acombination of two or more of the following proteins: p16, p53, Ki-67,beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S), matrixmetalloproteinase 1, CD1a, NF-kappa-B p65 (NF-κB), cyclo-oxygenase-2,CD68, CD4, forkhead box P3, CD45, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL and HIF-1alpha.

In some embodiments, the composition or system comprises a cell samplewith one or more different cell types. In some embodiments, the cellsample comprises a combination of any two or more of the following celltypes: epithelial cells, multilayered-epithelial cells, endothelialcells, peripheral mononuclear lymphocytes, T cells, B cells, naturalkiller cells, eosinophils, mast cells, macrophages, dendritic cells,neutrophils, fibroblasts, goblet cells, dysplastic cells, and non-gobletcolumnar epithelial cells.

In some embodiments the plurality of probes and/or stains comprises atleast one stain that binds nucleic acid. In some embodiments, theplurality of probes comprise at least one or a combination of probesthat identify the presence or absence of 9p21, 8q24.12-13, 17q11.2-q12,or centromeres.

In some embodiments, the composition or system creates an image withhigh resolution or a three-dimensional image.

In some embodiments, the invention relates to a method of quantifyingone or more biomarkers in a cell sample comprising: providing a cellsample, contacting a plurality of probes and/or stains with cell sampleeither serially or simultaneously, and determining relative quantity ofprobes bound to a plurality of biomarkers using a composition or systemcomprising: (a) a cell sample; (b) a plurality of probes and/or stainsthat bind or intercalate to biomarkers of the cell sample; (c) one ormore optical scanners that generates digital imaging data about thepresence, absence, location, quantity, and/or intensity of at least oneprobe or stain that binds a biomarker of the cell sample; (d) one ormore data processors that, either individually or collectively: (i)receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (iii) converts the one or more descriptivefeatures into a score, wherein (iii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into a score; (c) one or more monitors thatcomprises a screen and that receives a component of the digital images,or, optionally, receives the digital imaging signal from the dataprocessor and projects a digitally addressable image onto its screen;and (f) one or more data storage units; wherein the one or more opticalscanners, the one or more data processors, the one or more monitors, andthe one or more data storage units are in digital communication witheach other by a means to transmit digital data.

In some embodiments, the method comprises biomarkers derived from asingle cell. In some embodiments, the method comprises biomarkersderived from two or more cells. In some embodiments, the methodcomprises a cell sample or tissue sample prepared from a biopsy of asubject. In some embodiments, the method comprises a cell sampleprepared from a punch biopsy of a subject. In some embodiments, themethod comprises a cell sample prepared from a biopsy of a subjectdiagnosed with Barrett's esophagus or suspected of having Barrett'sesophagus. In some embodiments, the method comprises the cell sample isfrom a subject or group of subjects diagnosed with or suspected ofhaving Barrett's esophagus, Barrett's esophagus with high gradedysplasia, Barrett's esophagus with reactive atypia, or indeterminateBarrett's esophagus. In some embodiments, the method comprises the cellsample is from a subject or group of subjects that has been misdiagnosedwith a stage of Barrett's esophagus.

In some embodiments, the method comprises a plurality of probescomprising at least two probes that each comprises a fluorescent tag. Insome embodiments, the method comprises a system that measures thequantity or intensity of at least two probes measures fluorescence of atleast two fluorescent tags simultaneously. In some embodiments, thebiomarkers are chosen from a combination of two or more of the followingproteins: p16, p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase(AMACR, P504S), matrix metalloproteinase 1, CD1a, NF-kappa-B p65,cyclo-oxygenase-2, CD68, CD4, forkhead box P3, CD45, thrombospondin-1,C-myc, cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL and HIF-1alpha. In some embodiments, the methodcomprises a cell sample comprising a combination of any two or more ofthe following cell types: epithelial cells, multilayered-epithelialcells, endothelial cells, peripheral mononuclear lymphocytes, T cells, Bcells, natural killer cells, eosinophils, mast cells, macrophages,dendritic cells, neutrophils, fibroblasts, goblet cells, dysplasticcells, and non-goblet columnar epithelial cells. In some embodiments,the method comprises a plurality of probes and/or stains comprising atleast one stain that binds nucleic acid. In some embodiments, the methodcomprises a plurality of probes comprising at least one or a combinationof probes that identify the presence or absence of 9p21, 8q24.12-13,17q11.2-q12, or centromeres. In some embodiments, the method uses asystem that creates an image with high resolution or a three-dimensionalimage. In some embodiments, the quantified biomarkers are of at leastpartly known nucleic acid sequence, and the plurality of probescomprises a probe set for each nucleic acid to be quantified, the probeset comprising a plurality of probes perfectly complementary to anucleic acid sequence. In some embodiments, the method comprises aplurality of probes comprising a probe set for between 1 and about 20biomarkers. In some embodiments, the method comprises a plurality ofprobes comprising a probe set for between 1 and about 15 biomarkers. Insome embodiments, the method comprises a plurality of probes comprisinga probe set for between 1 and about 10 biomarkers.

In some embodiments, the method further comprises comparing the ratio ofbound probes to determine the relative expression levels of thebiomarkers.

In some embodiments, the invention relates to a method of quantifyingone or more biomarkers in a cell sample comprising: providing a cellsample, contacting a plurality of probes and/or stains with cell sampleeither serially or simultaneously, and determining relative quantity ofprobes bound to a plurality of biomarkers using a composition or systemcomprising: (a) a cell sample; (b) a plurality of probes and/or stainsthat bind or intercalate to biomarkers of the cell sample; (c) one ormore optical scanners that generates digital imaging data about thepresence, absence, location, quantity, and/or intensity of at least oneprobe or stain that binds a biomarker of the cell sample; (d) one ormore data processors that, either individually or collectively: (i)receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (iii) converts the one or more descriptivefeatures into a score, wherein (iii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into a score; (e) one or more monitors thatcomprises a screen and that receives a component of the digital images,or, optionally, receives the digital imaging signal from the dataprocessor and projects a digitally addressable image onto its screen;and (f) one or more data storage units; wherein the one or more opticalscanners, the one or more data processors, the one or more monitors, andthe one or more data storage units are in digital communication witheach other by a means to transmit digital data; and wherein the relativeexpression levels of 5 or more biomarkers are determined simultaneously.In some embodiments, the relative expression levels of 10 or morebiomarkers are determined simultaneously. In some embodiments, therelative expression levels of 15 or more biomarkers are determinedsimultaneously.

In some embodiments, the invention relates to a method of diagnosingBarrett's esophagus comprising: (a) providing a cell sample of tissue;(b) contacting a plurality of probes and/or stains with the cell sample;(c) identifying one or more descriptive features; (d) determining one ormore scores based upon the presence, absence, or quantity of descriptivefeatures; and (e) correlating the score to a subclass of Barrett'sesophagus. In some embodiments, the method further comprises identifyinga subject suspected of or having been previously diagnosed with agastrointestinal tract disorder, wherein the cell sample is taken fromthe subject suspected of or having been previously diagnosed with agastrointestinal tract disorder. In another embodiment, the method ofdiagnosing Barrett's esophagus comprises using any one of theaforementioned systems or compositions. In some embodiments, thedescriptive features comprise one or a combination of more than one ofmorphological features chosen from: the presence of goblet cells; thepresence of cytological and architectural abnormalities; the presence ofcell stratification; the presence of multilayered epithelium; thematuration of the surface epithelium; the degree of budding,irregularity, branching, and atrophy in crypts; the proportion of lowgrade crypts to high grade crypts; the presence of splaying andduplication of the muscularis mucosa; the presence, number and size ofthin-walled blood vessels, lymphatic vessels, and nerve fibers; thefrequency of mitoses; the presence of atypical mitoses; the size andchromicity of nuclei; the presence of nuclear stratification; thepresence of pleomorphism; the nucleus:cytoplasm volume ratio; thepresence of villiform change; the presence of the squamocolumnarjunction (Z-line) and its location in relation to the gastroesophagealjunction; the presence of ultra-short segment Barrett's esophagus; theintestinal differentiation in nongoblet columnar epithelial cells; thepresence of longated, crowded, hyperchromatic, mucin-depleted epithelialcells; the degree of loss of cell polarity; the penetration of cellsthrough the original muscularis mucosa; the infiltration of dysplasticcells beyond the basement membrane into the lamina propria. In someembodiments, the descriptive features are determined by measuring thepresence, absence, quantity, or copy number of probes and/or stainsbound to or intercalated with biomarkers derived from a single celltype.

In some embodiments, the biomarkers are expressed in two or more cells.In some embodiments, the probes and/or stains used in the methodcomprise a plurality of probes with one or more fluorescent tag. In someembodiments, the probes and/or stains used in the method comprise aplurality of stains that fluoresce when exposed to natural, visible, orUV light.

The invention also relates to a method of diagnosing Barrett's esophaguscomprising: (a) providing a cell sample of tissue; (b) contacting aplurality of probes and/or stains with the cell sample; (c) identifyingone or more descriptive features; (d) determining one or more scoresbased upon the presence, absence, or quantity of descriptive features;and (e) correlating the score to a subclass of Barrett's esophagus;wherein the cell sample comprises a tissue from a brushing, punchbiopsy, or surgical resection of a subject. In some embodiments, themethod further comprises identifying a subject suspected of or havingbeen previously diagnosed with a gastrointestinal tract disorder,wherein the cell sample is taken from the subject suspected of or havingbeen previously diagnosed with a gastrointestinal tract disorder. Insome embodiments, the method further comprises identifying a subject whois at risk of developing dysplasia, tumor growth, or malignant cancer inthe gastrointestinal tract, wherein the cell sample is taken from thesubject who has been identified as a subject at risk of developingdysplasia, tumor growth, or malignant cancer in the gastrointestinaltract.

In some embodiments, the method comprises the use of any aforementionedcomposition or system comprising an optical scanner, wherein the opticalscanner that measures the quantity or intensity of at least two probesmeasures fluorescence of at least two fluorescent tags simultaneously.

In some embodiments, the method comprises the use of any aforementionedcomposition or system comprising the detection of biomarkers expressedby cells in the cell sample, wherein the biomarkers are chosen from acombination of two or more of the following proteins: p16, p53, Ki-67,beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S), matrixmetalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4,forkhead box P3, CD45, thrombospondin-1, C-myc, cytokeratin-20,fibroblast activation protein alpha, cyclin D1, HER2/neu, EGFR,Interleukin-6, PLAU plasminogen activator urokinase (uPA), CDX2, Fas,FasL and HIF-1alpha. In some embodiments, the method comprises analyzinga cell sample comprising a plurality of cells, wherein the plurality ofcells comprise a combination of any two or more of the following celltypes: epithelial cells, multilayered-epithelial cells, endothelialcells, peripheral mononuclear lymphocytes, T cells, B cells, naturalkiller cells, eosinophils, mast cells, macrophages, dendritic cells,neutrophils, fibroblasts, goblet cells, dysplastic cells, and non-gobletcolumnar epithelial cells. In some embodiments, the probes and/or stainscomprise at least one stain that binds nucleic acid. In someembodiments, the probes and/or stains comprise at least one probe thatbinds nucleic acid. In some embodiments, the probes and/or stainscomprise at least one stain that intercalates to nucleic acid. In someembodiments, the method comprises a plurality of probes and/or stains,wherein the plurality of probes and/or stains comprise at least one or acombination of probes or stains that identify the presence or absence of9p21, 8q24.12-13, 17q11.2-q12, or centromeres.

The invention also relates to a method of diagnosing Barrett's esophaguscomprising: (a) providing a cell sample of tissue; (b) contacting aplurality of probes and/or stains with the cell sample; (c) identifyingone or more descriptive features; (d) determining one or more scoresbased upon the presence, absence, or quantity of descriptive features;and (c) correlating the score to a subclass of Barrett's esophagus;wherein the method comprises one of the aforementioned system orcomposition to identify one or more descriptive features, wherein thesystem or composition generates an image with high resolution or athree-dimensional image. In some embodiments, the one or moredescriptive features comprise quantification of a partly known nucleicacid sequence, and the wherein said quantification is determined byquantifying a probe set comprising a plurality of probes perfectlycomplementary or partially complementary to a nucleic acid sequence. Insome embodiments, the method comprises a plurality of probes and/orstains that comprise a probe set for between 1 and about 20 biomarkers.In some embodiments, the method comprises the plurality of probes and/orstains that comprise a probe set for between 1 and about 15 biomarkers.In some embodiments, the method comprises the plurality of probes and/orstains that comprise a probe set for between 1 and about 10 biomarkers.In some embodiments, the method comprises the plurality of probes and/orstains that comprise a probe set for between 1 and about 5 biomarkers.In some embodiments, the method comprises the plurality of probes and/orstains that comprise a probe set for between 1 and about 4 biomarkers.In some embodiments, the method comprises the plurality of probes and/orstains that comprise a probe set for between 1 and about 3 biomarkers.

The invention also relates to a method of diagnosing Barrett's esophaguscomprising: (a) providing a cell sample of tissue; (b) contacting aplurality of probes and/or stains with the cell sample; (c) identifyingone or more descriptive features; (d) determining one or more scoresbased upon the presence, absence, or quantity of descriptive features;and (e) correlating the score to a subclass of Barrett's esophagus;wherein the method comprises use of one of the aforementioned system orcomposition to complete any one or more steps (a), (b), (c), (d), and(e).

The invention also relates to a method of diagnosing Barrett's esophaguscomprising: (a) providing a cell sample of tissue; (b) contacting aplurality of probes and/or stains with the cell sample; (c) identifyingone or more descriptive features; (d) determining one or more scoresbased upon the presence, absence, or quantity of descriptive features;and (e) correlating the score to a subclass of Barrett's esophagus;wherein the method comprises one of the aforementioned system orcomposition to identify one or more descriptive features, whereinidentifying one or more descriptive features comprises comparing theratio of the specific binding of probe and/or stain to a biomarker tothe non-specific binding of probes and/or stains in order to determinethe relative expression levels of the biomarkers. The invention alsorelates to a method of diagnosing Barrett's esophagus comprising: (a)providing a cell sample of tissue; (b) contacting a plurality of probesand/or stains with the cell sample; (c) identifying one or moredescriptive features; (d) determining one or more scores based upon thepresence, absence, or quantity of descriptive features; and (e)correlating the score to a subclass of Barrett's esophagus; wherein themethod comprises one of the aforementioned system or composition toidentify one or more descriptive features, wherein identifying one ormore descriptive features comprises comparing the ratio of bound tounbound probes and/or stains to determine the relative expression levelsof the biomarkers. In some embodiments, the identifying one or moredescriptive features comprises analyzing the relative expression levelsof 2 or more biomarkers simultaneously. In some embodiments, theidentifying one or more descriptive features comprises analyzing therelative expression levels of 3 or more biomarkers simultaneously. Insome embodiments, the identifying one or more descriptive featurescomprises analyzing the relative expression levels of 4 or morebiomarkers simultaneously. In some embodiments, the identifying one ormore descriptive features comprises analyzing the relative expressionlevels of 5 or more biomarkers simultaneously. In some embodiments, theidentifying one or more descriptive features comprises analyzing therelative expression levels of 8 or more biomarkers simultaneously. Insome embodiments, the identifying one or more descriptive featurescomprises analyzing the relative expression levels of 10 or morebiomarkers simultaneously. In some embodiments, the identifying one ormore descriptive features comprises analyzing the relative expressionlevels of 12 or more biomarkers simultaneously. In some embodiments, theidentifying one or more descriptive features comprises analyzing therelative expression levels of 15 or more biomarkers simultaneously. Insome embodiments, the identifying one or more descriptive featurescomprises analyzing the relative expression levels of 20 or morebiomarkers simultaneously.

In some embodiments, the invention relates to a method of prognosing aclinical outcome of a subject comprising: (a) providing a cell sample;(b) contacting a plurality of probes and/or stains with the cell sample;(c) identifying one or more descriptive features; (d) determining one ormore scores based upon the presence, absence, or quantity of descriptivefeatures; and (e) correlating the score to a subclass of Barrett'sesophagus or a certain clinical outcome.

In some embodiments, the invention relates to a method of prognosing aclinical outcome of a subject comprising: (a) providing a cell sample;(b) contacting a plurality of probes and/or stains with the cell sample;(c) identifying one or more descriptive features; (d) determining one ormore scores based upon the presence, absence, or quantity of descriptivefeatures; and (e) correlating the score to a subclass of Barrett'sesophagus or a certain clinical outcome; wherein the method comprisesuse of one of the aforementioned systems or compositions to complete anyone or more steps (a), (b), (c), (d), and (e).

The invention also relates to a method of determining patientresponsiveness to a therapy for one or a combination of gastrointestinaltract disorders comprising: (a) providing a cell sample; (b) contactinga plurality of probes and/or stains with the cell sample; (c)identifying one or more descriptive features; (d) determining one ormore scores based upon the presence, absence, and/or quantity ofdescriptive features; and (e) predicting patient responsiveness to atherapy to treat or prevent a gastrointestinal disorder based upon thescore.

The invention also relates to a method of compiling a cellular systemsbiological profile comprising: (a) providing one or more cell samplesfrom a set of subjects; (b) contacting a plurality of probes and/orstains with the one or more cell samples; (c) identifying one or moredescriptive features for each cell sample; (d) determining one or morescores for each cell sample based upon the presence, absence, orquantity of descriptive features; and (e) compiling the scores of eachsubject; and, optionally, (f) stratifying each subject according to theone or more scores. In some embodiments, the subject or subjects areidentified as being susceptible to or at risk for developing, or havingbeen previously diagnosed with one or more gastrointestinal tractdisorders.

In some embodiments, the invention relates to a method of compiling acellular systems biological profile comprising: (a) providing one ormore cell samples from a set of subjects; (b) contacting a plurality ofprobes and/or stains with the one or more cell samples; (c) identifyingone or more descriptive features for each cell sample; (d) determiningone or more scores for each cell sample based upon the presence,absence, or quantity of descriptive features; (e) compiling the scoresof each subject; and, optionally, (f) stratifying each subject accordingto the one or more scores, further comprising correlating the scores ofeach subject with a diagnosis of one or more gastrointestinal disorders,a prognosis of a gastrointestinal disorder, or a responsiveness totherapy to treat or prevent one or more gastrointestinal disorders. Insome embodiments, the gastrointestinal disorder is Barrett's esophagusor a subclass thereof.

In some embodiments, the invention relates to a method of monitoringgene or protein expression in a subject comprising: contacting aplurality of probes and/or stains with a first and second cell sample ofa subject; determining the relative binding or intercalating of theplurality of probes and/or stains to biomarkers from the first andsecond cell samples; and comparing the presence, absence, or quantity ofbiomarkers from the first sample to the presence, absence, or quantityof biomarkers from the second cell sample.

In some embodiments, the invention relates to a method of classifyinggastrointestinal tract cell samples, comprising: determining a biomarkerexpression profile of each of a plurality of cell samples; andclassifying the cell samples in clusters determined by similarity ofbiomarker expression profile. In some embodiments, the method ofclassifying gastrointestinal tract cell samples further comprises use ofany one of the compositions or systems described herein.

The invention also relates to a method of monitoring differentiation,morphology, or progression of tumor growth, or the progression of tumormalignancy in a subject comprising: providing two or more cell samplesfrom said subject; determining an expression profile of each of the cellsamples; classifying the cell samples into clusters determined bysimilarity of biomarker expression profile; ordering the clusters bysimilarity of biomarker expression profile; and determining a timecourse of biomarker expression levels for each of the plurality ofbiomarkers at different stages of differentiation, morphology, or tumorgrowth progression in the cell samples.

The invention also relates to a method for identifying differentiallyexpressed biomarkers, comprising: determining a biomarker expressionprofile of each of a set of cell samples at different time points;classifying the profile in clusters determined by similarity ofbiomarker expression; ordering the clusters by similarity of biomarkerexpression; determining a time course of biomarker levels for each ofthe plurality of biomarkers at different time points; and identifyingdifferentially expressed biomarkers as between cell samples in the sameand different clusters. In some embodiments, the method comprisesidentifying differentially expressed biomarkers further comprises use ofany one of the compositions or systems described herein.

The invention also relates to a method of identifying a specific celltype within a cell sample that contains a plurality of cells comprising:determining a biomarker expression profile of a plurality of cells;classifying the plurality of cells in clusters determined by similarityof biomarker expression profile; and determining the nature and functionof the plurality of cells.

In some embodiments, the invention relates to a method of determining,testing, calculating, or assessing a risk of progression of Barrett'sesophagus in a subject comprising: a) detecting a subset of biomarkersin a sample from the subject, wherein two or more biomarkers in saidsubset are selected from the group consisting p53, HIF-1alpha,beta-catenin, and COX-2; and b) determining at least one or moredescriptive features listed in Table 4 or 5 associated with saidbiomarkers, wherein the presence, absence, location, ratio, or quantityof descriptive features determines a score, relative to a control,wherein the score correlates to the risk of progression of Barrett'sesophagus in the subject.

In another embodiment, the method of determining, testing, calculating,or assessing a risk of progression of Barrett's esophagus in a subjectcomprises: a) analyzing, locating, identifying, or quantifying a subsetof biomarkers in a sample from the subject, wherein two or morebiomarkers in said subset are selected from the group consisting p53,HIF-1alpha, beta-catenin, and COX-2; and b) determining at least one ormore descriptive features listed in Table 4 or 5 associated with saidbiomarkers, wherein the presence, absence, location, ratio, or quantityof descriptive features determines a score, relative to a control,wherein the score correlates to the risk of progression of Barrett'sesophagus in the subject.

In another embodiment, the sample comprises a brushing, biopsy, orsurgical resection of cells and/or tissue from the subject.

In another embodiment, the descriptive features are identified, located,analyzed, determined, or detected in subcellular and/or tissuecompartments.

In another embodiment, at least one or more biomarkers detected areselected from the group consisting of p16, Ki-67, alpha-methylacyl-CoAracemase (AMACR, P504S), matrix metalloproteinase 1, CD1a, NF-kappa-B,CD68, CD4, forkhead box P3, CD45RO, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, and FasL.

In another embodiment, the method further detects at least one or morebiomarkers selected from the group consisting of AMACR, CD1a, CD45RO,CD68, CK-20, Ki-67, NF-κB, and p16.

In another embodiment, the subject has an increased risk of progressionto low grade dysplasia, high grade dysplasia or esophageal cancer.

In another embodiment, the subject is diagnosed with no dysplasia,reactive atypia, indefinite for dysplasia, low grade dysplasia, or highgrade dysplasia.

In another embodiment, the sample is at room temperature or frozen. Inanother embodiment, the sample is freshly obtained, formalin fixed,alcohol fixed, or paraffin embedded.

In another embodiment, the method further comprises detecting the subsetof biomarkers using probes that specifically bind to each of saidbiomarkers. In another embodiment, at least 10, at least 20, at least30, at least 40, at least 50, or 60 descriptive features from Tables 4.In another embodiment, at least 10, at least 20, at least 30, at least40, at least 50, at least 60, at least 70, at least 80, or 89descriptive features from Table 5.

In another embodiment, the sample comprises a brushing, biopsy, orsurgical resection of cells and/or tissue from the subject.

In another embodiment, the descriptive features are identified, located,analyzed, determined, or detected in subcellular and/or tissuecompartments.

In another embodiment, the descriptive features further comprise one ormore morphometric markers selected from the group consisting of nucleararea, nuclear equivalent diameter, nuclear solidity, nucleareccentricity, gland to stroma ratio, nuclear area to cytoplasmic arearatio, glandular nuclear size, glandular nuclear size and intensitygradient, and nuclear texture.

In another embodiment, the sample is at room temperature or frozen. Inyet another embodiment, the sample is freshly obtained, formalin fixed,alcohol fixed, or paraffin embedded.

In another embodiment, the probes are fluorescent and/or comprise afluorescent tag, preferably wherein each probe is labeled with adifferent fluorophore.

In another embodiment, the subset of biomarkers comprises at least 3biomarkers and wherein the 3 biomarkers are an epithelial biomarker,immune biomarker and/or a stromal biomarker. In yet another embodiment,the method further detects a stem cell biomarker. In another embodiment,the method detects 2 or more, 3 or more, 4 or more, 5 or more, 8 ormore, or 12 or more biomarkers are determined simultaneously. In yetanother embodiment, the subject is a human.

In one embodiment, the invention relates to a method of classifyingBarrett's esophagus in a subject, comprising: a) detecting a subset ofbiomarkers in a sample from the subject, wherein two or more biomarkersare selected from the group consisting of HIF-1alpha, p53, CD45RO, p16,AMACR, CK-20, CDX-2, HER2/neu, CD1a, COX-2, NF-κB, and a nucleic acidbiomarker; and b) determining at least one or more descriptive featureslisted in Table 6 associated with said biomarkers, wherein the presence,absence, location, ratio, or quantity of descriptive features determinesa score, relative to a control, wherein the score correlates to theclassification of Barrett's esophagus.

In one embodiment, the invention relates to a method of classifyingBarrett's esophagus in a subject, comprising: a) analyzing, locating,identifying, or quantifying a subset of biomarkers in a sample from thesubject, wherein two or more biomarkers are selected from the groupconsisting of HIF-1alpha, p53, CD45RO, p16, AMACR, CK-20, CDX-2,HER2/neu, CD1a, COX-2, NF-κB, and a nucleic acid biomarker; and b)determining at least one or more descriptive features listed in Table 6associated with said biomarkers, wherein the presence, absence,location, ratio, or quantity of descriptive features determines a score,relative to a control, wherein the score correlates to theclassification of Barrett's esophagus.

In another embodiment, the method further detects at least one or morebiomarkers selected from the group consisting of Ki-67, beta-catenin,matrix metalloproteinase 1, CD68, CD4, forkhead box P3,thrombospondin-1, C-myc, fibroblast activation protein alpha, cyclin D1,EGFR, Interleukin-6, PLAU plasminogen activator urokinase (uPA), Fas,and FasL.

In another embodiment, the classification of Barrett's esophaguscomprises no dysplasia, reactive atypia, low grade dysplasia, and highgrade dysplasia.

In another embodiment, the method further comprises one or more probesthat specifically bind to each of the biomarkers.

In another embodiment, at least 10, at least 20, at least 30, at least40, at least 50, at least 60, at least 70, or 71 descriptive featuresfrom Table 6.

In another embodiment, the sample comprises a brushing, biopsy, orsurgical resection of cells and/or tissue from the subject.

In another embodiment, the descriptive features are identified, located,analyzed, determined, or detected in subcellular and/or tissuecompartments.

In another embodiment, the descriptive features further comprises one ormore morphometric markers selected from the group consisting of nucleararea, nuclear equivalent diameter, nuclear solidity, nucleareccentricity, gland to stroma ratio, nuclear area to cytoplasmic arearatio, glandular nuclear size, glandular nuclear size and intensitygradient, and nuclear texture.

In another embodiment, the sample is at room temperature or frozen. Inyet another embodiment, the sample is freshly obtained, formalin fixed,alcohol fixed, or paraffin embedded.

In another embodiment, the probes are fluorescent and/or comprise afluorescent tag, preferably wherein each probe is labeled with adifferent fluorophore.

In another embodiment, the subset of biomarkers comprises at least 3biomarkers and wherein the 3 biomarkers are an epithelial biomarker,immune biomarker and/or a stromal biomarker. In yet another embodiment,the method further detects a stem cell biomarker. In another embodiment,the method detects 2 or more, 3 or more, 4 or more, 5 or more, 8 ormore, or 12 or more biomarkers are determined simultaneously. In yetanother embodiment, the subject is a human.

In another embodiment, the invention relates to a kit for determining,testing, calculating, or assessing a risk of progression of Barrett'sesophagus in a subject comprising: a) one or more probes that is capableof detecting at least two or more biomarkers from the group consistingof p53, HIF-1alpha, beta-catenin, and COX-2; and b) instructions forusing the probes to determine one or more descriptive features togenerate a score from a cell and/or tissue sample of a subject.

In another embodiment, the kit further comprises probes that are capableof detecting at least one or more biomarkers detected are selected fromthe group consisting of p16, Ki-67, alpha-methylacyl-CoA racemase(AMACR, P504S), matrix metalloproteinase 1, CD1a, NF-kappa-B, CD68, CD4,forkhead box P3, CD45RO, thrombospondin-1, C-myc, cytokeratin-20,fibroblast activation protein alpha, cyclin D1, HER2/neu, EGFR,Interleukin-6, PLAU plasminogen activator urokinase (uPA), CDX2, Fas,and FasL.

In another embodiment, the kit further comprises probes that are capableof detecting at least one or more biomarkers selected from the groupconsisting of AMACR, CD1a, CD45RO, CD68, CK-20, Ki-67, NF-κB, and p16.

In another embodiment, at least 10, at least 20, at least 30, at least40, at least 50, or 60 descriptive features from Tables 4. In anotherembodiment, at least 10, at least 20, at least 30, at least 40, at least50, at least 60, at least 70, at least 80, or 89 descriptive featuresfrom Table 5.

In another embodiment, the present invention relates to a kit forclassifying Barrett's esophagus in a subject, comprising: a) one or moreprobes that is capable of detecting at least two or more biomarkers fromthe group consisting of HIF-1alpha, p53, CD45RO, p16, AMACR, CK-20,CDX-2, HER2, CD1a, COX-2, NF-κB, Ki-67, CD68, Beta-catenin, and nucleicacid; and b) instructions for using the probes to determine one or moredescriptive features to generate a score from a cell and/or tissuesample of a subject.

In another embodiment, the kit further comprises probes that are capableof detecting at least one or more biomarkers selected from the groupconsisting of Ki-67, beta-catenin, matrix metalloproteinase 1, CD68,CD4, forkhead box P3, thrombospondin-1, C-myc, fibroblast activationprotein alpha, cyclin D1, EGFR, Interleukin-6, PLAU plasminogenactivator urokinase (uPA), Fas, and FasL.

In another embodiment, at least 10, at least 20, at least 30, at least40, at least 50, at least 60, at least 70, or 71 descriptive featuresfrom Table 6.

In another embodiment, the score is predictive of the clinical outcomeof Barrett's esophagus in the subject and/or diagnostic of the subclassof Barrett's esophagus in the subject. In another embodiment, the probescomprise antibody probes that specifically bind to said biomarkers. Inanother embodiment, the probes are fluorescent and/or comprise afluorescent tag.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIG. 1 depicts a multiplexed fluorescence labeling and digital imagingof biomarkers in sections of various tissues including tonsil tissue.

FIG. 2 depicts a multiplexed fluorescence labeling and digital imagingof nuclei and biomarkers in sections of dysplastic Barrett's esophagusbiopsies.

FIG. 3 depicts digital image analysis to segment nuclei (dark greynuclei masks) and cells as individual objects (dark grey cell masks) andto identify Ki-67-positive (white masks) and Ki-67-negative cells (darkgrey masks) within a tissue sample.

FIG. 4 depicts Digital Fluorescence Images of Barrett's Esophagus withHigh Grade Dysplasia Biopsy Tissue Section Stained with BiomarkerSubpanel 1. A: Hoechst labeling (nuclei), B: Ki-67-Alexa Fluor 488, C:CK-20-Alexa Fluor 555, D: Beta-catenin-Alexa Fluor 647.

FIG. 5 depicts Digital Fluorescence Images of Esophageal Adenocarcinomain a Background of Barrett's Esophagus Biopsy Tissue Section Stainedwith Biomarker Subpanel 2. A: Hoechst labeling (nuclei), B: p16-AlexaFluor 488, C: AMACR-Alexa Fluor 555, D: p53-Alexa Fluor 647.

FIG. 6 depicts Digital Fluorescence Images of a Barrett's Esophagus withLow Grade Dysplasia Biopsy Tissue Section Stained with BiomarkerSubpanel 3. A: Hoechst labeling (nuclei), B: CD68-Alexa Fluor 488, C:NF-κB-Alexa Fluor 555, D: COX-2-Alexa Fluor 647.

FIG. 7 depicts Digital Fluorescence Images of a Barrett's EsophagusWithout Dysplasia Biopsy Tissue Section Stained with Biomarker Subpanel4. A: Hoechst labeling (nuclei), B: HIF1-alpha-Alexa Fluor 488, C:CD45RO-Alexa Fluor 555, D: CD1a-Alexa Fluor 647.

FIG. 8 depicts Digital Fluorescence Images of a Barrett's Esophagus WithHigh Grade Dysplasia Biopsy Tissue Section Stained with BiomarkerSubpanel 5. A: Hoechst labeling (nuclei), B: HER2/neu-Alexa Fluor 488,C: CK-20-Alexa Fluor 555, D: CDX-2-Alexa Fluor 647.

FIG. 9 depicts a Dashboard for Digital Tissue Image Segmentation andData Extraction.

FIG. 10 depicts Four Channel Fluorescence Biomarker Images and ImageSegmentation for Quantitative Biomarker and Morphology Analysis.

FIG. 11 depicts Receiver Operator Characteristics Curve Plot and BoxPlot for Multivariate Predictive Classifier to Stratify No Progressorsfrom Progressors to HGD/EAC.

FIG. 12 depicts Receiver Operator Characteristics Curves and Box Plotsfor Example Univariate Predictive Features to Stratify No Progressorsfrom Progressors to HGD/EAC.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

Various terms relating to the methods and other aspects of the presentinvention are used throughout the specification and claims. Such termsare to be given their ordinary meaning in the art unless otherwiseindicated. Other specifically defined terms are to be construed in amanner consistent with the definition provided herein.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contentclearly dictates otherwise.

The term “about” as used herein when referring to a measurable valuesuch as an amount, a temporal duration, and the like, is meant toencompass variations of ±20%, ±10%, ±5%, ±1%, or ±0.1% from thespecified value, as such variations are appropriate to perform thedisclosed methods. As used herein, the terms “increase” and “decrease”mean, respectively, to cause a statistically significantly (i.e., p<0.15) increase or decrease of at least 1%, 2%, or 5%.

As used herein, the recitation of a numerical range for a variable isintended to convey that the invention may be practiced with the variableequal to any of the values within that range. Thus, for a variable whichis inherently discrete, the variable is equal to any integer valuewithin the numerical range, including the end-points of the range.Similarly, for a variable which is inherently continuous, the variableis equal to any real value within the numerical range, including theend-points of the range. As an example, and without limitation, avariable which is described as having values between 0 and 2 takes thevalues 0, 1 or 2 if the variable is inherently discrete, and takes thevalues 0.0, 0.1, 0.01, 0.001, 10⁻¹², 10⁻¹¹, 10⁻¹⁰, 10⁻⁹, 10⁻⁸, 10⁻⁷,10⁻⁶, 10⁻⁵, 10⁻⁴ or any other real values ≥0 and ≤2 if the variable isinherently continuous.

As used herein, unless specifically indicated otherwise, the word “or”is used in the inclusive sense of “and/or” and not the exclusive senseof “either/or.”

The term “amino acid” refers to a molecule containing both an aminogroup and a carboxyl group bound to a carbon which is designated theα-carbon. Suitable amino acids include, without limitation, both the D-and L-isomers of the naturally occurring amino acids, as well asnon-naturally occurring amino acids prepared by organic synthesis orother metabolic routes. In some embodiments, a single “amino acid” mighthave multiple sidechain moieties, as available per an extended aliphaticor aromatic backbone scaffold. Unless the context specifically indicatesotherwise, the term amino acid, as used herein, is intended to includeamino acid analogs, naturally occurring amino acids, and non-naturallyamino acids.

The term “antibody” refers to an immunoglobulin molecule or fragmentthereof having a specific structure that interacts or binds specificallywith a molecule comprising an antigen. As used herein, the term“antibody” broadly includes full-length antibodies and may includecertain antibody fragments thereof. Also included are monoclonal andpolyclonal antibodies, multivalent and monovalent antibodies,multispecific antibodies (for example bi-specific antibodies), chimericantibodies, human antibodies, humanized antibodies and antibodies thathave been affinity matured. An antibody binds selectively orspecifically to a biomarker of a gastrointestinal disorder if theantibody binds preferentially to an antigen expressed by a cell and hasless than 25%, or less than 10%, or less than 1% or less than 0.1%cross-reactivity with a polypeptide expressed by a cell within thegastrointestinal tissue or cells derived from another tissue thatmigrates from one tissue to the gastrointestinal tissue. Usually, theantibody will have a binding affinity (dissociation constant (Kd)value), for the antigen or epitope of no more than 10⁻⁶M, or 10⁻⁷M, orless than about 10⁻⁸M, or 10⁻⁹M, or 10⁻¹⁰M, or 10⁻¹¹M or 10⁻¹²M. Bindingaffinity may be assessed using any method known by one of ordinary skillin the art, such as surface plasma resonance, immunoaffinity assays, orELISAs.

As used herein, the term “biomarker” means any analyte, metabolite,nucleic acid, amino acid sequence or fragments thereof, polyprotein,protein complex, molecule, or chemical compound that is produced,metabolized, catabolized, secreted, phagocytosed, or expressed by a cellor tissue and that provides a useful measure of the presence, absence,or quantity of a certain cell type or descriptive feature indicative of,characteristic of, or suggestive of a diagnosis of a particular diseaseor disorder.

As used herein, the term “epithelial biomarker” means any marker ofepithelial cell subset, e.g. normal gland or surface epithelium cell,metaplastic gland or surface epithelium cell, dysplastic gland orsurface epithelium cell, cancer cell of epithelial origin, or marker ofepithelial cell function, e.g. proliferation, cell cycle control, tumorsuppressor gene, oncogene, adhesion, migration, fatty acid metabolism,apoptosis, inflammation.

As used herein, the term “stromal biomarker” means any marker of stromalcell type, e.g. endothelial cell, fibroblast, or stromal cell function,e.g. angiogenesis, tissue remodeling.

As used herein, the term “immune biomarker” means any marker of immunecell subset, e.g. T lymphocyte, B lymphocyte, supressor cell, regulatoryT cell, dendritic cell, macrophage, granulocyte, or immune cellfunction, e.g. cytokines, chemokines, activation, cell-cell contact,proliferation, inflammation.

As used herein, the term “nucleic acid biomarker” means any specificlocus of a gene or DNA sequence measured with locus-specific probes,e.g. 9p21, 8q24.12-13, 17q11.2-q12. It also includes centromeresmeasured with centromere enumeration probes, e.g. chromosome 8, 9, 17.It also includes anything that binds to nucleic acid and can aid in thevisualization of the nucleus (e.g. Hoechst,4′,6-diamidino-2-phenylindole (DAPI)).

As used herein, the term “morphometric marker” means any measurement ofstructures, shapes, parts, sizes and textures of cells and tissues.Examples of morphometric markers include nuclear area, nuclearequivalent diameter, nuclear solidity, nuclear eccentricity, gland tostroma ratio, nuclear area to cytoplasmic area ratio, glandular nuclearsize, glandular nuclear size and intensity gradient, and nucleartexture. The term “nuclear equivalent diameter” means a scalar thatspecifies the diameter of a circle with the same area as the nuclearregion and can be computed as sqrt(4*Area/pi). It is an estimate of thediameter of nuclei, which are non-circular, irregularly-shaped objects.The term “nuclear solidity” means a scalar specifying the proportion ofthe pixels in the convex hull that are also in the nuclear region. It isequal to the ratio of nuclear area: convex area of nuclei based onfluorescent labeling of nuclei. The term “nuclear eccentricity” is ascalar that specifies the eccentricity of the nuclear ellipse that hasthe same second-moments as the nuclear region. The eccentricity is theratio of the distance between the foci of the ellipse and its major axislength. The value is between 0 and 1. The term “nuclear texture” is thespatial arrangements of fluorescently-labeled pixels in nuclei area.

As used herein, the term “stem cell biomarker” means any marker todistinguish stem cells from non-stem cells or marker of stem cellfunction.

In some embodiments, the disease is a gastrointestinal disorder. In someembodiments, the biomarker is chosen from one or more of the moleculesidentified in Table 1. In some embodiments, the biomarkers can be themeasure of receptor expression levels, transcription factor activation;location or amount or activity of a protein, polynucleotide, organelle,and the like; the phosphorylation status of a protein, etc. In oneembodiment, a biomarker is a nucleic acid (e.g., DNA, RNA, includingmicro RNAs, snRNAs, mRNA, rRNA, etc.), a receptor, a cell membraneantigen, an intracellular antigen, and extracellular antigen, asignaling molecule, a protein, and the like without limitation, lipids,lipoproteins, proteins, cytokines, chemokines, growth factors, peptides,nucleic acids, genes, and oligonucleotides, together with their relatedcomplexes, metabolites, mutations, variants, polymorphisms,modifications, fragments, subunits, degradation products, elements, andother analytes or sample-derived measures. A biomarker can also includea mutated protein or proteins, a mutated nucleic acid or mutated nucleicacids, variations in copy numbers, and/or transcript variants, incircumstances in which such mutations, variations in copy number and/ortranscript variants are useful for generating a predictive model, or areuseful in predictive models developed using related markers (e.g.,non-mutated versions of the proteins or nucleic acids, alternativetranscripts, etc.).

The term “biomaterial or biomaterials” means any protein, tissue,molecule, extracellular matrix component, biostructure, membrane,subcellualr compartment or any combination of the above that is derivedfrom a cell and/or is spatially positioned outside of cell in a cellsample.

As used herein, the terms “a biomarker expression profile” means acollection of data collected by a user related to the quantity,intensity, presence, absence, or spatial distribution of a biomarker orset of biomarkers assigned to a cell or biomaterial or subcellularcompartment, each within a cell sample.

As used herein, the term “cell sample” means a composition comprising anisolated cell or plurality of cells. In some embodiments, the cellsample comprises an individual cell. In some embodiments, the cellsample is a composition comprising a plurality of cells. In someembodiments, the cell sample is a tissue sample taken from a subjectwith a gastrointestinal disorder. In one embodiment, the cell sample isa tissue sample. In some embodiments, the cell sample comprises aplurality of cells from the gastrointestinal tract. In some embodiments,the cell sample is a plurality of esophageal cells. In some embodiments,the cell sample is freshly obtained, formalin fixed, alcohol-fixedand/or paraffin embedded. In some embodiments, the cell sample is abiopsy isolated from a subject who has been diagnosed or is suspected oridentified as having one or more gastrointestinal disorders. In oneembodiment, the cell sample a biopsy isolated from a subject who hasbeen diagnosed or is suspected or identified as having Barrett'sesophagus. In another embodiment, the cell sample comprises a tissuefrom a brushing, punch biopsy, or surgical resection of a subject. Inone embodiment of the invention, the one or more tissue samples areisolated from one or more animals. For example, in one embodiment, theone or more animals are one or more humans. In a particular embodiment,one or more cell samples are isolated from a human patient at one ormore time points, such that at least one tissue sample is isolated fromeach time point from the same patient. In some embodiments, a cellsample can include a single cell or multiple cells or fragments of cellsor an aliquot of body fluid, taken from a subject, by means includingvenipuncture, excretion, ejaculation, massage, biopsy, needle aspirate,lavage sample, scraping, surgical incision, or intervention or othermeans known in the art. In another embodiment, the invention includesobtaining a cell sample associated with a subject, where the sampleincludes one or more biomarkers. The sample can be obtained by thesubject or by a third party, e.g., a medical professional. Examples ofmedical professionals include physicians, emergency medical technicians,nurses, first responders, psychologists, medical physics personnel,nurse practitioners, surgeons, dentists, and any other obvious medicalprofessional as would be known to one skilled in the art. A sample caninclude peripheral blood cells, isolated leukocytes, or RNA extractedfrom peripheral blood cells or isolated leukocytes. The sample can beobtained from any bodily fluid, for example, amniotic fluid, aqueoushumor, bile, lymph, breast milk, interstitial fluid, blood, bloodplasma, cerumen (earwax), Cowper's fluid (pre-ejaculatory fluid), chyle,chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears,vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid,cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreoushumour. In an example, the sample is obtained by a blood draw, where themedical professional draws blood from a subject, such as by a syringe.The bodily fluid can then be tested to determine the value of one ormore descriptive features using the interpretation function or methodsdescribed herein. The value of the one or more descriptive features canthen be evaluated by the same party that performed the method using themethods of the invention or sent to a third party for evaluation usingthe methods of the invention. In one embodiment of the invention, themethod comprises obtaining or isolating at least two cell samples fromone or more subjects. In one embodiment of the invention, the methodcomprises obtaining or isolating at least three cell samples from one ormore subjects. In one embodiment of the invention, the method comprisesobtaining or isolating at least four cell samples from one or moresubjects. Any suitable tissue sample can be used in the methodsdescribed herein. For example, the tissue can be epithelium, muscle,organ tissue, nerve tissue, tumor tissue, and combinations thereof. Inone embodiment, the cell sample is not derived from blood, sera, orblood cells. In one embodiment, the cell sample is not derived fromcells of the liver, pancreas, gallbladder, bladder, skin, heart, lungs,kidneys, spleen, bone marrow, adipose tissue, nervous system,circulatory system, or lymphatic system. Samples of tissue can beobtained by any standard means (e.g., biopsy, core puncture, dissection,and the like, as will be appreciated by a person of skill in the art).In some embodiments, at least one cell sample is labeled with ahistological stain, to produce a histologically stained cell sample. Asused in the invention described herein, histological stains can be anystandard stain as appreciated in the art, including but not limited to,alcian blue, Fuchsin, haematoxylin and eosin (H&E), Masson trichrome,toluidine blue, Wright's/Giemsa stain, and combinations thereof. In someembodiment, as will be appreciated by a person of skill in the art,traditional histological stains are not fluorescent. At least one othersection is labeled with a panel of fluorescently labeled reagents toproduce a fluorescently labeled section. As used in the inventiondescribed herein, the panel of fluorescently labeled reagents comprisesa number of reagents, such as fluorescently labeled antibodies,fluorescently labeled peptides, fluorescently labeled polypeptides,fluorescently labeled aptamers, fluorescently labeled oligonucleotides(e.g. nucleic acid probes, DNA, RNA, cDNA, PNA, and the like),fluorescently labeled chemicals and fluorescent chemicals (e.g., Hoechst33342, propidium iodide, Draq-5, Nile Red, fluorescently labeledphalloidin, 4′,6-diamidino-2-phenylindole (DAPI)), and combinationsthereof.

“Cellular systems biology” is defined as the of the interacting cellularand molecular networks of normal, tumor, immune, stromal and stem cellsin tissues and bodily fluids that give rise to normal function anddisease. Cells in tissues, as complex systems, exhibit properties thatare not anticipated from the analysis of individual components, known asemergent properties that require analysis of many factors tocharacterize cellular and molecular states. In some embodiments,correlation between measurements in individual cells is required toidentify and interpret cellular responses to drug treatment. A cellularsystems biological profile can be utilized to capture or compile a setof epidemiological data about a patient or subject population. In someembodiments, the subject population is a patient population at anelevated risk for developing Barrett's esophagus, suffering fromBarrett's esophagus, or having been diagnosed with Barrett's esophagus.All kits and methods of the present invention may also be used tocompile data around epidemiological data about a patient or subjectpopulation. All kits and methods of the present invention may also beused to acquire and track the progression of a particular disease ordisorder of a subject. In some embodiments, the subject population is apatient population at an elevated risk for developing Barrett'sesophagus, suffering from Barrett's esophagus, or having been diagnosedwith Barrett's esophagus. In some embodiments, particular expressionlevels of biomarkers are tracked and patient histories are compiled inorder to more finely characterize a patient's disease as falling into aparticular subclass of Barrett's esophagus.

“Clinical factor” is defined as a measure of a condition of a subject,e.g., disease activity or severity. “Clinical factor” encompasses allbiomarkers of a subject's health status, including non-sample markers,and/or other characteristics of a subject, such as, without limitation,age and gender, and clinical history related to other ailments,disorders, diseases, or the risk associated with developing suchailment, disorder, or disease. A clinical factor can be a score, avalue, or a set of values that can be obtained from evaluation of a cellsample (or plurality of samples) from a subject or a subject under adetermined condition. A clinical factor can also be predicted bybiomarkers and/or other parameters such as gene expression surrogates.

As used herein, the term “classifying Barrett's esophagus” meansassigning a diagnostic subcategory or risk score to a subject.Diagnostic subcategories include:

Barrett's esophagus, no dysplasia

Barrett's esophagus, reactive atypia

Barrett's esophagus, indefinite for dysplasia

Barrett's esophagus, low grade dysplasia

Barrett's esophagus, high grade dysplasia

Esophageal adenocarcinoma

As used herein, the term “control” means healthy esophageal tissue,Barrett's esophagus tissue with no dysplasia, Barrett's esophagus tissuefrom a subject that did not progress to low grade or high gradedysplasia, or esophageal carcinoma.

As used herein, the term “converting” means subjecting the one or moredescriptive features to an interpretation function or algorithm for apredictive model of disease. In some embodiments, the disease isBarrett's esophagus or a subclass of

Barrett's esophagus. In some embodiments, the interpretation functioncan also be produced by a plurality of predictive models. In one of thepossible embodiments, the predictive model would include a regressionmodel and a Bayesian classifier or score. In one embodiment, aninterpretation function comprises one or more terms associated with oneor more biomarker or sets of biomarkers. In one embodiment, aninterpretation function comprises one or more terms associated with thepresence or absence or spatial distribution of the specific cell typesdisclosed herein. In one embodiment, an interpretation functioncomprises one or more terms associated with the presence, absence,quantity, intensity, or spatial distribution of the morphologicalfeatures of a cell in a cell sample. In one embodiment, aninterpretation function comprises one or more terms associated with thepresence, absence, quantity, intensity, or spatial distribution ofdescriptive features of a cell in a cell sample.

As used herein, “transmutes said digital imaging data into a digitalimaging signal” means the process of a data processor that receivesdigital imaging data and converts the digital code of said digitalimaging data into a code compatible with the software used to create animage of a cell sample visible to a user.

As used herein, “descriptive features” are defined as values associatedwith data measurements, a series of data measurements, observations, ora series of observations about a cell sample, typically evidenced by thepresence, absence, quantity, localization or spatial proximity to otherdescriptive features or biomarkers relative space within a cell sample.Examples of descriptive features include values calculated through animage interpretation function, measured or quantified by standard orknown microscopy techniques, but are not limited to values associatedwith the presence, absence, localization, or spatial distribution of oneor more biomarkers. Examples of descriptive features include valuescalculated through an image interpretation function, measured orquantified by standard or known microscopy techniques, but are notlimited to values associated with the presence, absence, localization,or spatial distribution of one or more biomarkers chosen from: proteinpost-translational modifications such as phosphorylation, proteolyticcleavage, methylation, myristoylation, and attachment of carbohydrates;translocations of ions, metabolites, and macromolecules betweencompartments within or between cells; changes in the structure andactivity of organelles; and alterations in the expression levels ofmacromolecules such as coding and non-coding RNAs and proteins. In someembodiments, descriptive features comprise values associated with one ora combination of more than one of the following morphological featuresof a cell or cell sample chosen from: the presence of goblet cells; thepresence of cytological and architectural abnormalities; the presence ofcell stratification; the presence of multilayered epithelium; thematuration of the surface epithelium; the degree of budding,irregularity, branching, and atrophy in crypts; the proportion of lowgrade crypts to high grade crypts; the presence of splaying andduplication of the muscularis mucosa; the presence, number and size ofthin-walled blood vessels, lymphatic vessels, and nerve fibers; thefrequency of mitoses; the presence of atypical mitoses; the size andchromicity of nuclei; the presence of nuclear stratification; thepresence of pleomorphism; the nucleus:cytoplasm volume ratio; thepresence of villiform change; the presence of the squamocolumnarjunction (Z-line) and its location in relation to the gastroesophagealjunction; the presence of ultra-short segment Barrett's esophagus; theintestinal differentiation in nongoblet columnar epithelial cells; thepresence of longated, crowded, hyperchromatic, mucin-depleted epithelialcells; the degree of loss of cell polarity; the penetration of cellsthrough the original muscularis mucosa; the infiltration of dysplasticcells beyond the basement membrane into the lamina propria. In someembodiments, the descriptive feature may represent a numerical valueestimated by an operator of the apparati or compositions disclosedherein using methods of quantifying such biomarkers as it is known inthe art. In some embodiments, the descriptive feature comprises a valueor values associated with the presence, absence, proximity, localizationrelative to one or more biomarkers, or quantity of one or more of thefollowing cell types: epithelial cells, multilayered-epithelial cells,endothelial cells, peripheral mononuclear lymphocytes, T cells, B cells,natural killer cells, eosinophils, mast cells, macrophages, dendriticcells, neutrophils, fibroblasts, goblet cells, dysplastic cells, andnon-goblet columnar epithelial cells. In some embodiments, thedescriptive feature comprises value related to the presence, absence,localization or relative proximity to other descriptive features orbiomarkers, or quantity of one or more of the following biomarkersinside or outside a cell: p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha.The detection of a biomarker in one or more sections is a read-out ofone or more descriptive features of a cellular systems biology profile.In some embodiments, a “descriptive feature” refers to a characteristicand/or a value which relates to a measurement or series of measurementsrelated to a particular biomarker (which can indicate the location,function, spatial distribution, presence or absence of the biomarkermade within a cell sample. Biological functions include, but are notlimited to: protein posttranslational modifications such asphosphorylation, proteolytic cleavage, methylation, myristoylation, andattachment of carbohydrates; translocations of ions, metabolites, andmacromolecules between compartments within or between cells; changes inthe structure and activity of organelles; and alterations in theexpression levels of macromolecules such as coding and non-coding RNAsand proteins, morphology, state of differentiation, and the like. Asingle biomarker can provide a read-out of more than one feature. Forexample, Hoechst dye detects DNA, which is an example of a biomarker. Anumber of features can be identified by the Hoechst dye in the cellsample such as nucleus size, cell cycle stage, number of nuclei,presence of apoptotic nuclei, etc.

As used herein, the term “derived from” in the context of therelationship between a cell or amino acid sequence and a relatedbiomarker or related amino acid sequence describes a biomarker orrelated amino acid sequence that may be homologous to or structurallysimilar to the related chemical structure or related amino acidsequence.

As used herein, the term “digitally addressable” means an image that canbe viewed, manipulated, or accessed by the user with software.

As used herein, the terms “gastrointestinal disorder” refers to anydisease or abnormality related to the alimentary canal including but notnecessarily limited to one or more of the following conditions:abdominal pain, gastroesophageal reflux disease (GERD), constipation,diarrhea, diverticulosis, gastrointestinal bleeding, stomach cancer,esophageal cancer, intestinal cancer, colon cancer, Barrett's esophagus,irritable bowel disease, infectious colitis, ulcerative colitis, Crohn'sdisease, ischemic colitis, radiation colitis, irritable bowel syndrome,acute perforation, ileus, appendicitis, intra-abdominal abscesses,intestinal obstruction, gastritis, autoimmune metaplastic atrophicgastritis, ulcers in the stomach, peptic ulcer disease, dyspepsia,gastrointestinal stromal tumors, small bowel tumors, levator syndrome,pilonidal disease, proctits, fistulkas, fissures, incontinence

The terms “highly correlated gene expression” or “highly correlatedmarker expression” refer to biomarker expression values that have asufficient degree of correlation to allow their interchangeable use in apredictive model of Barrett's esophagus. For example, if gene x havingexpression value X is used to construct a predictive model, highlycorrelated gene y having expression value Y can be substituted into thepredictive model in a straightforward way readily apparent to thosehaving ordinary skill in the art and the benefit of the instantdisclosure. Assuming an approximately linear relationship between theexpression values of genes x and y such that Y=a+bX, then X can besubstituted into the predictive model with (Y-a)/b. For non-linearcorrelations, similar mathematical transformations can be used thateffectively convert the expression value of gene y into thecorresponding expression value for gene x. The terms “highly correlatedmarker” or “highly correlated substitute marker” refer to markers thatcan be substituted into and/or added to a predictive model based on, forinstance, the above criteria. A highly correlated marker can be used inat least two ways: (1) by substitution of the highly correlatedbiomarker(s) for the original biomarker(s) and generation of a new modelfor predicting Barrett's esophagus risk; or (2) by substitution of thehighly correlated biomarker(s) for the original biomarker(s) in theexisting model for predicting a subject's propensity to develop, risk todevelop, or diagnosis or Barrett's esophagus or a subclass of Barrett'sesophagus.

As used herein, the term “instructions” refers to materials and methodsfor staining tissue slides with the probes, imaging the probes on thetissue slides, analyzing the images to extract the biomarker data and/orthe processing the data into a score.

As used herein, the term “location” refers to a subcellular compartmentor tissue compartment. Subcellular compartments include the nucleus,cytoplasm, plasma membrane, and nuclear membrane. Tissue compartmentsinclude the surface epithelium, glands, stroma, and tumor.

As used herein, the term “probe” refers to any molecule that binds orintercalates to a biomarker, either covalently or non-covalently. Insome embodiments, the probes include probe sets which include one ormore probes that bind a single biomarker. The term “probe set” issometime interchangeable for a panel of two or more probes that allowthe detection of one or more biomarkers. In some embodiments the probeor probes are fluorescently labeled. In some embodiments, eachfluorescently labeled probe is specific for at least one biomarker. Inone embodiment of the invention, the panel of fluorescently labeledprobes detects at least about two different biomarkers. In oneembodiment of the invention, the panel of fluorescently labeled probesdetects at least about three different biomarkers. In one embodiment ofthe invention, the panel of fluorescently labeled probes detects atleast about four different biomarkers. In one embodiment of theinvention, the panel of fluorescently labeled probes detects at leastabout five different biomarkers. In another embodiment of the invention,the panel of fluorescently labeled probes detects at least about four toabout six, to about ten, to about twelve different biomarkers or more.In another embodiment of the invention, the panel of fluorescentlylabeled probes detects at least about three different biomarkers. In afurther embodiment, each fluorescently labeled probe has differentfluorescent properties, which are sufficient to distinguish thedifferent fluorescently labeled probes in the panel.

As used herein, the term “ratio” means the ratio of one biomarker'squantity to a different biomarker's quantity in the same or differentsubcellular compartment or tissue compartment. It can also mean theratio of one biomarker's quantity in a subcellular compartment toquantity of same biomarker in another subcellular compartment within thesame cell. It can also mean the ratio of one biomarker's quantity in atissue compartment to quantity of same biomarker in another tissuecompartment within the same biopsy.

As used herein, the term “risk of progression” means the probability ofprogressing to low grade dysplasia, high grade dysplasia, or esophagealadenocarcinoma.

The term “score” refers to a single value that can be used as acomponent in a predictive model for the diagnosis, prognosis, orclinical treatment plan for a subject, wherein the single value iscalculated by combining the values of descriptive features through aninterpretation function or algorithm. In some embodiments, the subjectis suspected of having, is at risk of developing, or has been diagnosedwith a gastrointestinal disorder. In another embodiment the subject issuspected of having or is at risk of developing Barrett's esophagus or asubclass of Barrett's esophagus. Risk scores are scores of 1-100, with 1indicating lowest risk of progression and 100 indicating highest risk ofprogression. Risk classes are be low, intermediate and high.

The term “subclass of Barrett's esophagus” refers to any presentation ofBarrett's esophagus classified as having any common combination of oneor more descriptive features. In some embodiments, a subclass ofBarrett's esophagus refers to one of the following conditions: Barrett'sesophagus, no dysplasia, no progression in 5 years; Barrett's esophagus,no dysplasia, progression to low/high grade dysplasia in 5 years;Barrett's esophagus, indefinite for dysplasia, no progression in 5years; Barrett's esophagus, indefinite for dysplasia, progression tolow/high grade dysplasia or adenocarcinoma in 5 years; Barrett'sesophagus, reactive atypia; Barrett's esophagus, low grade dysplasia, noprogression in 5 years; Barrett's esophagus, low grade dysplasia,progression to high grade dysplasia or adenocarcinoma in 5 years;Barrett's esophagus, high grade dysplasia; Esophageal adenocarcinomaarising in a background of Barrett's esophagus.

In some embodiments, a subclass of Barrett's esophagus refers to one ofthe following conditions: low-grade dysplasia, high-grade dysplasia,reactive atypia, or indeterminate Barrett's esophagus. In someembodiments of the invention, the compositions and systems describedherein are designed to stratify patient groups more precisely anddiagnose the different subclasses of Barrett's esophagus moreaccurately.

The term “optical scanner” is used throughout the specification todescribe any device or series of devices that generates image data froma cell sample or set of cell samples. In some embodiments, opticalscanner is used to describe any device or series of devices thatgenerates digital image data from a cell sample or set of cell samples.In some embodiments, the optical scanner may be a microscope attached toa optical device that generates digital image data, which, when sent toimage forming apparatus such as a laser printer, a barcode reader, aconfocal scanning laser microscope, or an imaging display (monitor), canproduce an image visible to a user.

The term “subject” is used throughout the specification to describe ananimal from which a cell sample is taken. In some embodiment, the animalis a human. For diagnosis of those conditions which are specific for aspecific subject, such as a human being, the term “patient” may beinterchangeably used. In some instances in the description of thepresent invention, the term “patient” will refer to human patientssuffering from a particular disease or disorder. In some embodiments,the subject may be a human suspected of having or being identified as atrisk to develop a gastrointestinal disorder. In some embodiments, thesubject may be a human suspected of having or being identified as atrisk to develop Barrett's esophagus. In some embodiments, the subjectmay be a mammal which functions as a source of the isolated cell sample.In some embodiments, the subject may be a non-human animal from which acell sample is isolated or provided. The term “mammal” encompasses bothhumans and non-humans and includes but is not limited to humans,non-human primates, canines, felines, murines, bovines, equines, andporcines.

The terms “treating” and “to treat”, mean to alleviate symptoms,eliminate the causation either on a temporary or permanent basis, or toprevent or slow the appearance of symptoms. The term “treatment”includes alleviation, elimination of causation (temporary or permanent)of, or prevention of symptoms and disorders associated with anycondition. The treatment may be a pre-treatment as well as a treatmentat the onset of symptoms.

In one embodiment of the invention, provided is a method for producing acellular systems biology profile of one or more cell samples. As usedherein, “cellular systems biology” (also referred to herein as systemscell biology), is the investigation of the integrated and interactingnetworks of genes, proteins, and metabolites that are responsible fornormal and abnormal cell functions. In some embodiments, a cellularsystems biology profile refers to a systemic characterization of cellsin the context of a cell sample architecture such that the cells haveparticular characteristics dependent upon the relationships of differentcells within a cell sample and the biological or medical state of thetissue when isolated from a subject. It is the interactions,relationships, and spatial orientation of the biomarkers of orbiomaterials derived from a cell or cells from a cell sample that givesrise to the descriptive features that are used to construct a profile.The interrelationships within a cellular systems biology profile aredefined or calculated, for example, either arithmetically (e.g., ratios,sums, or differences between descriptive features) or statistically(e.g., hierarchical clustering methods or principal component analysesof combinations of descriptive values). In a particular embodiment, acellular systems biology profile defines the interrelationships betweena combination of at least about two descriptive features collected froma cell or cells within a cell sample. In a particular embodiment, acellular systems biology profile defines the interrelationships betweena combination of at least about three descriptive features collectedfrom a cell or cells within a cell sample. In a particular embodiment, acellular systems biology profile defines the interrelationships betweena combination of at least about four descriptive features collected froma cell or cells within a cell sample. In a particular embodiment, acellular systems biology profile defines the interrelationships betweena combination of at least about five descriptive features collected froma cell or cells within a cell sample. In another embodiment, a cellularsystems biology profile is the combination of at least about six, seven,eight, nine, ten, eleven, twelve, or more descriptive features or valuesassigned to the descriptive features.

The presence, absence, localization or spatial distribution of,proximity to other biomarkers, or quantity of one or more biomarkers ofthe invention can be indicated as a value. A value can be one or morenumerical values resulting from evaluation of a cell sample under acondition. The values can be obtained, for example, by experimentallyobtaining measurements from a cell sample by using one of the systems orcompositions disclosed herein. The values can be obtained, for example,by experimentally obtaining digital imaging data from a cell sample byusing one of the systems or compositions disclosed herein. The valuescan be obtained, for example, by experimentally obtaining measurementsfrom a cell sample by performing one of the methods described herein.Alternatively, one of ordinary skill in the art can obtain a digitalimaging data from a service provider such as a laboratory, or from adatabase or a server on which the digital imaging data has been stored,e.g., on a storage memory.

System Components

The invention relates to a system comprising: a cell sample; a pluralityof probes and/or stains; one or more optical scanners; one or more dataprocessors; one or more data storage units; one or more monitors;wherein the one or more optical scanners, the one or more dataprocessors, the one or more monitors, and the one or more data storageunits are in digital communication with each other by a means totransmit digital data. In some embodiments the system comprises a cellsample isolated from a subject with a gastrointestinal disorder. In someembodiments, the system comprises a cell sample isolated from a subjectwith Barrett's esophagus or a subclass of Barrett's esophagus. In someembodiments, the cell sample is isolated from a subject suspected ofhaving, being at risk for developing, or diagnosed with agastrointestinal disorder. In some embodiments, the cell sample isisolated from a subject suspected of having, being at risk fordeveloping, or diagnosed with Barrett's esophagus or a subclass ofBarrett's esophagus.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includingquantitative expression or spatial distribution data for at least onebiomarker set selected from the group consisting of the biomarker setsin term 1, term 2, and term 3; wherein terms 1 through 3 include anycombination of one or more biomarkers p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/ncu, EGFR, Interleukin-6, PLAUplasminogen activator urokinasc (uPA), CDX2, Fas, FasL and HIF-1alpha.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includingquantitative expression or spatial distribution data for at least onebiomarker sets selected from the group consisting of the biomarker setsin term 1, term 2, term 3, and term 4; wherein terms 1 through 4 includeany combination of one or more biomarkers p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha;and one or more data processors are operably coupled to the data storageunit, units, or memory for determining a score with an interpretationfunction wherein the score is predictive of a risk of developing orbeing diagnosed with Barrett's esophagus in the subject; and wherein atleast one of the terms relates to the spatial distribution of one ormore biomarkers. In some embodiments, the one or more data processorsare remotely operated over a network. In some embodiments, the one ormore data processors are remotely operated over a digital network.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includingquantitative expression or spatial distribution data for at least onebiomarker set selected from the group consisting of the biomarker setsin term 1, term 2, and term 3; wherein term 1 includes Ki-67, term 2includes beta-catenin, and term 3 includes the presence of nucleistained by Hoescht stain, and wherein terms 1, 2, and 3 optionallyinclude any one or more of the following biomarkers:alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha.In some embodiments, any of the methods described herein may contain adescriptive feature identified through computer recognition of thepresence, absence, quantity, intensity, or spatial distribution ofmorphological components of the cell. In some embodiments, the terms ofthe biomarker sets may be added to calculate the score.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includingquantitative expression or spatial distribution data for at least onebiomarker sets selected from the group consisting of the biomarker setsin term 1, term 2, term 3, and term 4; wherein terms 1 through 4 includeany combination of one or more biomarkers p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha;and one or more data processors are operably coupled to the data storageunit, units, or memory for determining a score with an interpretationfunction wherein the score is predictive of a risk of developing orbeing diagnosed with Barrett's esophagus in the subject; and wherein atleast one of the terms relates to the spatial distribution of one ormore biomarkers.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includequantitative expression or spatial distribution data for at least onebiomarker sets selected from the group consisting of the marker sets interm 1, term 2, term 3, term 4, and term 5; wherein terms 1 through 5include any combination of one or more biomarkers p16, p53, Ki-67,beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S), matrixmetalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4,forkhead box P3, CD45, thrombospondin-1, C-myc, cytokeratin-20,fibroblast activation protein alpha, cyclin D1, HER2/neu, EGFR,Interleukin-6, PLAU plasminogen activator urokinase (uPA), CDX2, Fas,FasL and HIF-1alpha; and a data processor communicatively coupled to thedata storage unit, units, or memory for determining a score with aninterpretation function wherein the score is predictive of Barrett'sesophagus in the subject; and wherein at least one of the terms relatesto the spatial distribution of one or more biomarkers.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includequantitative expression or spatial distribution data for at least onebiomarker sets selected from the group consisting of the marker sets interm 1, term 2, term 3, term 4, term 5, and term 6; wherein terms 1through 6 include any combination of one or more biomarkers p16, p53,Ki-67, beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S),matrix metalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2,CD68, CD4, forkhead box P3, CD45, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL and HIF-1alpha; and a data processorcommunicatively coupled to the data storage unit, units, or memory fordetermining a score with an interpretation function wherein the score ispredictive of Barrett's esophagus in the subject; and wherein at leastone of the terms relates to the spatial distribution of one or morebiomarkers.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit or memory forstoring one or more descriptive features associated with a cell sampleobtained from the subject, wherein the descriptive features includequantitative expression or spatial distribution data for at least onebiomarker sets selected from the group consisting of the marker sets interm 1, term 2, term 3, term 4, term 5, term 6, and term 7; whereinterms 1 through 7 include any combination of one or more biomarkers p16,p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S),matrix metalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2,CD68, CD4, forkhead box P3, CD45, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL and HIF-1alpha; and a data processorcommunicatively coupled to the data storage unit, units, or memory fordetermining a score with an interpretation function wherein the score ispredictive of Barrett's esophagus in the subject; and wherein at leastone of the terms relates to the spatial distribution of one or morebiomarkers.

Also described herein is a system for predicting Barrett's esophagus ina subject, the system including: a data storage unit, units or memoryfor storing one or more descriptive features associated with a cellsample obtained from the subject, wherein the descriptive featuresinclude quantitative expression data for at least one biomarker setsselected from the group consisting of the marker sets in term 1, term 2,term 3, term 4, optionally term 5, optionally term 6, and optionallyterm 7; wherein terms 1 through 7 include any combination of one or morebiomarkers p16, p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase(AMACR, P504S), matrix metalloproteinase 1, CD1a, NF-kappa-B p65,cyclo-oxygenase-2, CD68, CD4, forkhead box P3, CD45, thrombospondin-1,C-myc, cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinasc(uPA), CDX2, Fas, FasL and HIF-1alpha; and a data processorcommunicatively coupled to the data storage unit, units, or memory fordetermining a score with an interpretation function wherein the score ispredictive of Barrett's esophagus in the subject.

Also described herein is a computer-readable storage medium storingcomputer-executable program code, the program code including: programcode for storing a dataset of descriptive features associated with acell sample obtained from the subject, wherein the first datasetincludes quantitative expression data for at least one marker setselected from the group consisting of the marker sets in term 1, term 2,term 3, optionally term 4, optionally term 5, optionally term 6, andoptionally term 7; wherein wherein terms 1 through 7 include anycombination of one or more biomarkers p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha;and program code for determining a score with an interpretation functionwherein the score is predictive of Barrett's esophagus in the subject.

In some embodiments, the invention relates to software on an electronicmedium or system comprising such software used to correlate the clustergroups of biomarker, morphologic and clinical data features into indicesuseful to distinguish one or more particular cell types from a mixtureof cell types in a cell sample automatically.

In some embodiments, the invention relates to software on an electronicmedium or system comprising such software used to correlate the clustergroups of biomarker, morphologic and clinical data features into indicesuseful to predict the responsiveness a patient to a particular therapy.

In some embodiments, the invention relates to software on an electronicmedium or system comprising such software used to correlate the clustergroups of biomarker, morphologic and clinical data features into indicesuseful to predict one or more clinical treatment schedules for a patientautomatically.

In some embodiments, the invention relates to software on an electronicmedium or system comprising such software used to correlate the clustergroups of biomarker, morphologic and clinical data features into indicesuseful to predict the risk of developing one or more diseases orconditions automatically.

Generation and Analysis of Digital Imaging Data

The quantity of one or more biomarkers of the invention can be indicatedas a value. A value can be one or more numerical values resulting fromevaluation of a sample under a condition. The values can be obtained,for example, by experimentally obtaining measurements from a cell sampleby an performing an assay in a laboratory, or alternatively, obtaining adataset from a service provider such as a laboratory, or from a databaseor a server on which the dataset has been stored, e.g., on a storagememory. In an embodiment, a cell sample is obtained or provided by asubject. In some embodiments, the methods or compositions comprising thecell sample or set of cell samples comprise the identification of thesubject as having a gastrointestinal disorder. In some embodiments,methods of generating or analyzing the cell sample or set of cellsamples comprise the identification of the subject as having anincreased risk to develop a gastrointestinal disorder. In someembodiments, methods of generating or analyzing the cell sample or setof cell samples comprise the identification of the subject as having areduced risk to develop a gastrointestinal disorder as compared to thegeneral population. In some embodiments, methods of generating oranalyzing the cell sample or set of cell samples comprise theidentification of the subject as not having a gastrointestinal disorder.In some embodiments, methods of generating or analyzing the cell sampleor set of cell samples comprise the identification of the subject ashaving a Barrett's esophagus. Once identified, the cell samples areprovided to perform an analysis of the cell or plurality of cells,biomaterial or biomaterials within the cell sample. In one embodiment ofthe invention, a cell sample or a dataset of descriptive featuresderived from the cell sample are analyzed by one or more data processorsthat either, individually or collectively: (i) analyzes the digitalimage data to identify, measure, or quantify one or more descriptivefeatures from the plurality of probes and/or stains; and (ii) convertsthe one or more descriptive features into a score, wherein (ii)optionally comprises integrating stored data about a subject or group ofsubjects to convert the one or more descriptive features into a score.In some embodiments, the invention relates to a system that comprises:(a) a cell cell sample; (b) a plurality of probes and/or stains thatbind to biomarkers of the cell sample; and (c) Datasets associated withdescriptive features one or more data processors that either,individually or collectively: (i) analyzes the digital image data toidentify, measure, or quantify one or more descriptive features from theplurality of probes and/or stains; and (ii) converts the one or moredescriptive features into a score, wherein (ii) optionally comprisesintegrating stored data about a subject or group of subjects to convertthe one or more descriptive features into a score.

Descriptive features of the tissue are determined by performingmicroscopy on a cell sample or set of cell samples in parallel or insequence. In some embodiments, the descriptive features may be imagedand quantified by brightfield microscopy or fluorescent microscopy or adevice that performs both brightfield and fluorescent microscopy by useof one or more wavelength filters. In some embodiments, the microscopeis in operable communication to one or more data processors.

In one embodiment, the invention relates to a system or apparatus thatcomprises one or more data processors, each in operable communicationwith at least one optical scanner, that: (a) receives digital imagedata; and (b) may optionally transmute said digital imaging data into adigital imaging signal, which can be create a digital image of the cellsample; and (c) analyzes the digital image data to identify, measure, orquantify one or more descriptive features from a plurality of probesand/or stains that bind to biomarkers of the cell sample. In someembodiments, the system or apparatus optionally comprises one or moredata storage units, each in operable communication with at least oneprocessor. The analysis of the digital image data is performed by theone or more data processors that creates datasets associated with thepresence, absence, quantity or spatial distribution of two or morebiomarkers.

In an embodiment, a descriptive feature can include one clinical factoror a plurality of clinical factors. In an embodiment, a clinical factorcan be included within a dataset. A dataset can include one or more, twoor more, three or more, four or more, five or more, six or more, sevenor more, eight or more, nine or more, ten or more, eleven or more,twelve or more, thirteen or more, fourteen or more, fifteen or more,sixteen or more, seventeen or more, eighteen or more, nineteen or more,twenty or more, twenty-one or more, twenty-two or more, twenty-three ormore, twenty-four or more, twenty-five or more, twenty-six or more,twenty-seven or more, twenty-eight or more, twenty-nine or more, orthirty or more overlapping or distinct clinical factor(s). A clinicalfactor can be, for example, the condition of a subject in the presenceof a disease or in the absence of a disease. Alternatively, or inaddition, a clinical factor can be the health status of a subject.Alternatively, or in addition, a clinical factor can be age, gender,chest pain type, neutrophil count, ethnicity, disease duration,diastolic blood pressure, systolic blood pressure, a family historyparameter, a medical history parameter, a medical symptom parameter,height, weight, a body-mass index, resting heart rate, andsmoker/non-smoker status. Clinical factors can include whether thesubject has stable chest pain, whether the subject has been diagnosedwith a hiatial hernia, whether the subject has GERD, whether the subjecthas an gastritis, whether the subject has been previously diagnosed withBarrett's esophagus, whether the subject has had a gastorintestinalprocedure, whether the subject has diabetes, whether the subject has aninflammatory condition, whether the subject has an infectious condition,whether the subject is taking a steroid, whether the subject is takingan immunosuppressive agent, and/or whether the subject is taking achemotherapeutic agent.

The compiled dataset is converted into a score or scores, which then canbe used to correlate the descriptive features of the cell sample into abiological profile or predictive outcome for the subject. Biologicaloutcomes

Biomarkers and Descriptive Features

The quantity of one or more biomarkers of the invention can be indicatedas a descriptive feature, provided in terms of a value. The quantity isthe amount of any specific biomarker in a cellular or tissuecompartment. The signal intensity for each biomarker in the tissueimages is directly proportional to the biomarker quantity. A value canbe one or more numerical values resulting from analysis of a cell sampleunder a condition. The values can be obtained, for example, by obtainingan image of a cell sample.

In an embodiment, the quantity of one or more markers can be one or morenumerical values associated with expression levels of: p16, p53, Ki-67,beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S), matrixmetalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4,forkhead box P3, CD45, thrombospondin-1, C-myc, cytokeratin-20,fibroblast activation protein alpha, cyclin D1, HER2/neu, EGFR,Interleukin-6, PLAU plasminogen activator urokinase (uPA), CDX2, Fas,FasL and HIF-1alpha; resulting from evaluation of a cell sample under acondition. This nomenclature is used to refer to human genes inaccordance with guidelines provided by the Human Genome Organization(HUGO) Gene Nomenclature Committee (HGNC). Further information abouteach human gene, such as accession number(s) and aliases, can be foundby entering the gene name into the search page on the HGNC Searchgenenames.org website. For example, entering the term “CD45” into theSimple Search field of the HGNC website on Feb. 14, 2011 returns theapproved gene name of PTPRC (protein tyrosine phosphatasc, receptor typeC, the sequence accession IDs of Y00062 and NM_002838 and the previoussymbols of CD45.

Also described herein is a computer-implemented method for scoring acell sample or plurality of cell samples obtained from a subject,including: obtaining a first dataset associated with the first sample,wherein the first dataset includes quantitative expression data for atleast two markers selected from the group consisting of p16, p53, Ki-67,beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S), matrixmetalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4,forkhead box P3, CD45, thrombospondin-1, C-myc, cytokeratin-20,fibroblast activation protein alpha, cyclin D1, HER2/neu, EGFR,Interleukin-6, PLAU plasminogen activator urokinase (uPA), CDX2, Fas,FasL and HIF-1alpha; and determining, by a computer processor, a firstscore from the first dataset using an interpretation function, whereinthe first score is predictive of Barrett's esophagus in the subject orclass of subjects.

In an embodiment, a biomarker's associated value can be included in adigital imaging data associated with a sample obtained from a subject. Adataset can include the marker expression value or quantity of two ormore, three or more, four or more, five or more, six or more, seven ormore, eight or more, nine or more, ten or more, eleven or more, twelveor more, thirteen or more, fourteen or more, fifteen or more, sixteen ormore, seventeen or more, eighteen or more, nineteen or more, twenty ormore, twenty-one or more, twenty-two or more, twenty-three or more,twenty-four or more, twenty-five or more, twenty-six or more,twenty-seven or more, twenty-eight or more, twenty-nine or more, orthirty or more marker(s). For example, a dataset can include valuescorresponding to the presence, absence, quantity, location, or spatialrelationship between and among: 9p21, 8q24.12-13, 17q11.2-q12 orcentromeres.

In an embodiment, one or more markers can be divided into terms. Termscan include one marker, but generally include three or more markers.Terms can be included in a digital imaging data associated with a cellsample obtained or isolated from a subject. The dataset can include oneor more terms, two or more terms, three or more terms, four or moreterms, five or more terms, six or more terms, seven or more terms, eightor more terms, nine or more terms, or ten or more terms. In anembodiment, a term can include one or more, two or more, three or more,four or more, five or more, six or more, seven or more, eight or more,nine or more, ten or more, eleven or more, twelve or more, thirteen ormore, fourteen or more, fifteen or more, sixteen or more, seventeen ormore, eighteen or more, nineteen or more, twenty or more, twenty-one ormore, twenty-two or more, twenty-three or more, twenty-four or more,twenty-five or more, twenty-six or more, twenty-seven or more,twenty-eight or more, twenty-nine or more, or thirty or more marker(s).In an embodiment, the markers are divided into distinct terms: term 1,term 2, term 3, term 4, term 5, term 6, and term 7. In anotherembodiment, certain terms correspond to certain biomarkers. One ofseveral image analysis environments can be used to extract biomarkersand descriptive features from digital images as described in Mulrane,L., Rexhepaj, E., Penney, S., Callanan, J. J., Gallagher, W. M. (2008)Automated image analysis in histopathology: a valuable tool in medicaldiagnostics. Expert Review of Molecular Diagnostics, 8, 707-725.

In some embodiments, the system or apparatus comprises a plurality ofprobes and/or stains that bind one or more of the biomarkers in Table 1.All of the combinations of the biomarkers in Table 1 are contemplated bythe invention.

TABLE 1 Table of TissueCipher Biomarkers (Normal = normal esophagealtissue without Barrett's metaplasia). Biomarkers are categorizedaccording to their major functions. Biomarker Biomarker Specific NCBIGene ID, Other designations/ measurements and Categories Biomarkers fullname also known as relevant ranges Proliferation MKI67 (Ki-67) 4288,Antigen KI-67 1-99% cells positive antigen recognized by for Ki-67,measure monoclonal antibody proliferation index of Ki-67 epithelial,immune and stromal cells, average intensity 1.25-100 fold higher versusnormal Cell Type CD45 5788, LCA; LY5; B220; 1-99% cells positive MasksProtein tyrosine CD45; T200; CD45R; for CD45, provide phosphatase,receptor GP180; PTPRC mask of immune cells type, C within tissueCytokeratin-20 54474, K20; CD20; CK20; 1-99% positive for (CK-20)Keratin 20 CK-20; KRT21; cytokeratin-20, MGC35423; KRT20 provide mask ofepithelial cells or dysplastic cells within tissue Differentiation CDX21045, CDX2 caudal CDX3; CDX-3; CDX2 1-99% cells positive type homeobox 2for CDX2, average intensity 1.25-100 fold higher versus normal Apoptosisp53 7157, OTTHUMP00000221340; 1 99% cells positive tumor protein p53antigen NY-CO-13; for p53, average cellular tumor antigen intensity1.25-100 fold p53; p53 tumor higher versus normal suppressor;phosphoprotein p53; transformation-related protein 53 Fas 355, FAS Fas(TNF APT1; CD95; FAS1; 1-99% cells positive receptor superfamily, APO-1;FASTM; for Fas, average member 6) ALPS1A; TNFRSF6; intensity 1.25-100fold FAS higher versus normal, ratio of Fas:FasL across tissue and insingle cells FasL 356, FASLG Fas ligand FASL; CD178; CD95L; 1-99% cellspositive (TNF superfamily, CD95-L; TNFSF6; for FasL, average member 6)APT1LG1; FASLG intensity 1.25-100 fold higher versus normal, ratio ofFas:FasL across tissue and in single cells Cell Cycle p16 1029, ARF;MLM; P14; P16; 1-99% cells negative Control cyclin-dependent kinase P19;CMM2; INK4; for p16, average inhibitor 2A MTS1; TP16; CDK4I; intensity1.25-100 (melanoma, p16, CDKN2; INK4A; MTS- fold less than normalinhibits CDK4) 1; P14ARF; P19ARF; P16INK4; P16INK4A; P16-INK4A; CDKN2ACyclin D1 595, CCND1 cyclin D1 BCL1; PRAD1; 1-99% cells positive U21B31;D11S287E; for Cyclin D1, average CCND1 intensity 1.25-100 fold higherversus normal C-MYC v-myc MRTL; c-Myc; 1-99% cells positivemyelocytomatosis bHLHe39; MYC for C-MYC, average viral oncogeneintensity 1.25-100 homolog (avian) fold higher versus normal GrowthFactor HER2/neu 2064, ERBB2 v-erb-b2 NEU; NGL; IIER2; 1-99% cellspositive Receptors erythroblastic leukemia TKR1; CD340; HER-2; forHER2/neu, average viral oncogene homolog MLN 19; HER-2/neu; intensity1.25-100 fold 2, neuro/glioblastoma ERBB2 higher versus normal derivedoncogene homolog (avian) EGFR 1956, EGFR epidermal ERBB; HER1; mENA;1-99% cells positive growth factor receptor ERBB1; PIG61; EGFR for EGFR,average intensity 1.25-100 fold higher versus normal Metabolism Alpha-23600, RM; RACE; CBAS4; 1-99% cells positive methylacyl-CoAAlpha-methylacyl-CoA AMACR; p504s for AMACR, average racemase racemaseintensity 1.25-100 fold (AMACR) higher versus normal InflammationNuclear factor- 5970, v-rel Nuclear factor NF- Ratio of kappa-B p65reticuloendotheliosis kappa-B p65 subunit; nuclear:cytoplasmic/non-subunit (NF-kB viral oncogene homolog nuclear factor of kappa nuclearNF-κB p65 p65) A (avian) light polypeptide gene 0.1-100 enhancer inB-cells 3; transcription factor p65; v-rel avian reticuloendotheliosisviral oncogene homolog A (nuclear factor of kappa light polypeptide geneenhancer in B- cells 3 (p65)); v-rel reticuloendotheliosis viraloncogene homolog A, nuclear factor of kappa light polypeptide geneenhancer in B- cells 3, p65 Cyclo- 5743, PGH synthase 2; PHS 1-99% cellspositive oxygenase 2 Prostaglandin- 11; cyclooxygenase 2b; for COX-2,average (COX-2) endoperoxide synthase cyclooxygenase-2; intensity1.25-100 fold 2 (prostaglandin G/H prostaglandin G/H higher versusnormal synthase and synthase 2; cyclooxygenase) prostaglandin G/Hsynthase and cyclooxygenase; prostaglandin H2 synthase 2 Immune CD68968, CD68 antigen; 1-99% cells positive, Responses CD68 moleculemacrophage antigen ratio of CD68+ cells to CD68; macrosialin; CK-20+cells or p53+ scavenger receptor class cells D, member 1 CD1a 909, CD1amolecule CD1A antigen, a 1-50% cells positive polypeptide; T-cell forCD1a surface antigen T6/Leu- 6; T-cell surface glycoprotein CD1a;cluster of differentiation 1 A; cortical thymocyte antigen CD1A;differentiation antigen CD1-alpha-3; epidermal dendritic cell markerCD1a; hTal thymocyte antigen CD4 920, CD4 antigen (p55); 1-50% cellspositive CD4 molecule CD4 receptor; T-cell for CD4, 1-50% cells surfaceantigen T4/Leu- positive for both CD4 3; T-cell surface and FOXP3glycoprotein CD4 Forkhead box 50943, JM2; AIID; IPEX; 1-50% cellspositive P3 (FOXP3) Forkhead box P3 PIDX; XPID; DIETER; for FOXP3, 1-50%MGC141961; cells positive for both MGC141963; FOXP3 FOXP3 and CD4 IL63569, IL6 interleukin 6 HGF; HSF; BSF2; IL-6; 1-99% cells positive(interferon, beta 2) IFNB2; IL6 for IL-6, intensity of IL-6 1.25-100fold higher versus normal Angiogenesis HIF-1α 3091, HIF1A hypoxia HIF1;MOP1; PASD8; 1-99% cells positive inducible factor 1, alpha bHLHe78;HIF-1alpha; for HIF-1α, intensity subunit (basic helix- HIF1-ALPHA;HIF1A. of HIF-1α 1.25-100 loop-helix transcription fold higher versusfactor) normal Adhesion, uPA 5328, PLAU ATF; UPA; URK; u- 1-99% cellspositive Invasion, plasminogen activator, PA; PLAU for uPA, intensity ofMetastasis urokinase IL-6 1.25-100 fold higher versus normal Matrix4312, Fibroblast collagenase; 1-99% cells positive metalloproteinasematrix metallopeptidase interstitial collagenase; for MMP1, average 1(MMP1) 1 (interstitial matrix metalloprotease 1 intensity 1.25-100 foldcollagenase) higher versus normal Beta-catenin 1499, catenin (cadherin-CTNNB; FLJ25606; 1-99% cells positive associated protein), betaFLJ37923; for beta-catenin, ratio 1, 88 kDa DKFZp686D02253; ofnuclear:non-nuclear CTNNB1 signal 0.1-100, average intensity 1.25-100fold higher versus normal Stromal Fibroblast 2191, 170 kDa melanoma1-99% cells positive Processes activation fibroblast activationmembrane-bound for FAPα, intensity of protein alpha protein, alphagelatinase; FAPα 1.25-100 fold (FAPα) OTTHUMP00000207304; higher versusnormal integral membrane serine protease; seprase Thrombospondin- 7057,Thrombospondin-1 Thrombospondin-1, 1-99% cells positive 1 (TSP1) p180for TSP1, intensity of TSP1 1.25-100 fold higher versus normalAmplification, 9p21 1029, cyclin-dependent P16 (CDKN2A) gene 0-2 signalsper nuclei gains and losses kinase inhibitor 2A loci on chromosome 9 ofgene loci (melanoma, p16, inhibits CDK4) 8q24.12-13 4609, C-MYC geneloci on 0-200 signals per v-myc chromosome 8 nuclei myelocytomatosisviral oncogene homolog (avian) 17q11.2-q12 2064, RBB2 v-erb-b2 HER2 geneloci on 0-100 signals per erythroblastic leukemia chromosome 17 nucleiviral oncogene homolog 2, neuro/gliobla stoma derived oncogene homolog(avian) Chromosome n/a CEP9 0-4 signals per enumeration nuclei,identification probe 9 and enumeration of chromosome 9, used fornormalization of 9p21 signals Chromosome n/a CEP8 0-4 signals perenumeration nuclei, identification probe 8 and enumeration of chromosome8, used for normalization of 8q24.12-13 signals Chromosome n/a CEP17 0-4signals per nuclei, enumeration identification and probe 17 enumerationof chromosome 17, used for normalization of 17q11.2-q12 signalsAnalysis of Digital Imaging Data

The invention relates to optical scanning equipment, digital imagingequipment, or other scanner that generates digital imaging data aboutthe presence, absence, location, quantity, and/or intensity of at leastone probe or stain that binds a biomarker of the cell sample; and one ormore data processors that, either individually or collectively: (i)receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from at least one probeand/or stain. In some embodiments, the data processor comprises opticalscanning equipment and digital imaging equipment that generates digitalimaging data about the presence, absence, location, quantity, and/orintensity of at least one probe or stain that binds a biomarker of thecell sample; and one or more data processors that, either individuallyor collectively: (i) receives the digital image data from the opticalscanner and, optionally, transmutes said digital imaging data into adigital imaging signal; and (ii) analyzes the digital image data toidentify, measure, or quantify one or more descriptive features from theplurality of probes and/or stains. In another embodiment, the inventionrelates to a single device that comprises digital imaging equipment suchas an optical scanner and a data processor that collectively: (a)generate digital imaging data about the presence, absence, location,quantity, and/or intensity of at least one probe or stain that binds abiomarker of the cell sample; (b) receive the digital image data fromthe optical scanner and, optionally, transmutes said digital imagingdata into a digital imaging signal which becomes projected on a monitorfor viewing by an operator; and (c) analyze the digital image data toidentify, measure, or quantify one or more descriptive features from theplurality of probes and/or stains.

In some embodiments, the analysis of the digital image data is performedby algorithms developed by devices that perform known algorithms and,optionally create an image by progressive scan, line scan, arcatransference or optical matrix scan. In some embodiments, the analysisof the digital image data is performed by one of many commerciallyavailable devices in the art such as: Scan Scope Systems (AperioTechnologies Inc.), Aphelion (ADCIS), Aureon Pathomatrix or AureonDiscoveryPath (Aureon Laboratories), the BLISS workstation (BacusLaboratories), TMAx (Beecher Instruments), GenoMx VISION (Biogenex),PATHIAM or TissueAnalytics System (Biolmagene, Inc.), Automated CellularImagain System III (Dako), CELLENGER (Definiens), AQUA (HistoRx),Disovery-1 or Discovery TMA (Molecular Devices, Corp), VisioMorph(Visopharm), HistoQuant (3DHistech), algorithms designed by SlidePath.

In some embodiments analysis of a digital image may be performed by anyone of the methods described in U.S. Pat. Nos. 7,893,988, 7,860,292,7,844,125, 7,826,649, 7,787,674, 7,738,688, 7,689,024, 7,668,362,7,646,495, 7,602,524, 7,518,652, 7,502,519, 7,463,761, 7,457,446,7,428,324, 7,257,268, 7,116,440, 7,035,478; each of which areincorporated by reference in their entirety.

In some embodiments analysis of a digital image may be performed by anyone of the methods described in U.S. patent application Ser. No.11/709,601 (US Application No. 20080008349), US Application No.20080137937, or US Application No. 20080292153 which are incorporated byreference in their entirety.

In some embodiments, the analysis of the digital image data is performedby measuring patterns present in the pixel values of digital imagesusing a computer-implemented network structure. The network structureincludes a process hierarchy, a class network and a data network. Thedata network represents information associated with each pixel locationin the form of image layers, thematic layers and object networks. Theanalysis system performs both pixel-oriented processing andobject-oriented processing by using that combination of datarepresentations that yields the fastest result. Pixel-oriented andobject-oriented processing is combined so that fewer computations andless memory are used to analyze an acquired digital image. The datanetwork includes image layers of pixel values associated with pixellocations that are linked to objects of object networks. Each objectnetwork has various layers of objects (also called object “levels”). Theobjects of the data network are classified into classes of the classnetwork. The data network also includes thematic layers. Thematic layersare used in combination with the image layers and the object networks toanalyze digital images. There is a one-to-one relationship between apixel location and the thematic class of a thematic layer. For example,in one application, operations are performed on the pixel valuesassociated with an object depending on the thematic class linked to eachpixel location that is linked to the object. However, the analysissystem can also analyze digital images without using thematic layers.

In a specification mode and before the pixel values are acquired, theuser of the analysis system specifies the class network and the processhierarchy. The classes of the class network describe categories ofobjects that the user expects to find in the digital image. The useralso specifies thematic classes that describe categories of pixelvalues. The process hierarchy describes how the digital image is to beanalyzed in order to find a target object. The process hierarchy definesthe process steps performed on the pixel values and objects. In thespecification mode, the user also specifies types of links that are toconnect process steps, classes and objects of the data network to eachother. A link between two nodes describes the relationship between thetwo nodes.

In an execution mode, the analysis system performs the process steps onthe acquired pixel values. By performing the process steps, pixellocations associated with particular pixel values are linked to objects,and the objects are categorized as belonging to specific classes of theclass network. Pixel locations associated with particular pixel valuesare also categorized as belonging to one of the thematic classes. Theanalysis system links the process steps, classes and objects to eachother in a manner that enables the analysis system to detect a targetobject that is defined by a class. For example, the analysis system canrecognize where a predefined pattern occurs in the digital image.

Object-oriented image analysis can better recognize patterns in complexdigital images than can pure pixel-oriented statistical processing.However, object-oriented processing is computationally more intensiveand therefore slower than pure statistical processing. The more accuratepattern recognition of object-oriented image analysis can be retained,while at the same time reducing the amount of computations required, bycombining object-oriented and pixel-oriented processing. For example, anobject in a digital image can be analyzed by performing statisticalprocessing only on pixel values associated with pixel locations that arelinked to specific objects of an object network. In step one, a user ofthe analysis system specifies class network by defining the likelihoodthat objects of data network will belong to each particular class ofclass network. The user of the analysis system is, for example, aresearch doctor who is applying his expert knowledge to train theanalysis system in the specification mode. In step two, the userspecifies process hierarchy. The user specifies not only the individualprocess steps, but also the order in which the process steps are to beexecuted in the execution mode. In step three, the user specifies afilter and the user specifies the parameters of the filter. An exampleof a filter parameter is the size of the object to be filtered. The sizecan be defined as the border length of the object or the diameter of theobject, measured in pixel units. In step four, the analysis systemacquires the pixel values of first image layer. In step five, theanalysis system runs in the execution mode and generates a data networkby selectively linking pixel locations to objects according to the classnetwork and the process hierarchy. Each object is generated by linkingto the object pixel locations associated with pixel values havingsimilar characteristics. In step six, a new image layer is generated byperforming pixel-oriented processing only on those pixel values of firstimage layer whose pixel locations are linked to specific objects offirst object network. In this manner, the computations required toanalyze target patterns in the digital image are reduced, and the speedat which the patterns are recognized and measured is increased.

Morphological analysis of the tissue may be conducted by eithervisualizing the tissue or using an algorithm to measure the biomarkerexpression and other measurements from the analysis above to themorphology of the tissue consider importance of such measurements withrespect to their spatial distribution. For instance, in one embodiment,a cell sample is provided from a healthy subject or a subject that hasbeen identified as not having or having a low risk of developingBarrett's esophagus. Another cell sample is provided from a subjectsuspected as having Barrett's esophagus or identified as havingBarrett's esophagus. In one embodiment, any of the methods providedherein comprise providing a cell sample taken from a subject identifiedas having Barrett's esophagus. The morphological aspects of the two cellsamples are compared so that the relative frequency of biomarkers areassessed. In some embodiments, at least one or more of the followingmorphological aspects of the cell samples are compared: the presence ofgoblet cells; the presence of cytological and architecturalabnormalities; the presence of cell stratification; the presence ofmultilayered epithelium; the maturation of the surface epithelium; thedegree of budding, irregularity, branching, and atrophy in crypts; theproportion of low grade crypts to high grade crypts; the presence ofsplaying and duplication of the muscularis mucosa; the presence, numberand size of thin-walled blood vessels, lymphatic vessels, and nervefibers; the frequency of mitoses; the presence of atypical mitoses; thesize and chromicity of nuclei; the presence of nuclear stratification;the presence of pleomorphism; the nucleus:cytoplasm volume ratio; thepresence of villiform change; the presence of the squamocolumnarjunction (Z-line) and its location in relation to the gastroesophagealjunction; the presence of ultra-short segment Barrett's esophagus; theintestinal differentiation in nongoblet columnar epithelial cells; thepresence of longated, crowded, hyperchromatic, mucin-depleted epithelialcells; the degree of loss of cell polarity; the penetration of cellsthrough the original muscularis mucosa; the infiltration of dysplasticcells beyond the basement membrane into the lamina propria. In someembodiments, the spatial relationships among certain morphologicalaspects are compared. For example, a cell sample taken from a healthysubject or a subject identified as not having Barrett's esophagus mayhave very limited or completely absent intestinal differentiation innongoblet columnar epithelial cells. In contrast, a cell sample takenfrom a subject suspected as having or having been identified as havingBarrett's esophagus will have a moderate or high degree of intestinaldifferentiation in nongoblet columnar epithelial cells in spatiallyclustered positions among points in the tissue as compared to the cellsample from the healthy subject or the subject identified as not havingBarrett's esophagus.

In some embodiments, the scores will be determined based upon thepresence, absence, relative quantity, or spatial distribution of one ofthe following morphological features in the cell sample provided ascompares to a cell sample taken from a subject having been identified asbeing at an increased risk of developing Barrett's esophagus or anothergastrointestinal disorder. In some embodiments, the scores will bedetermined based upon the presence, absence, or spatial distribution, orrelative quantity of one of the following morphological features ascompared to a cell sample taken from a subject having been identifiedhaving Barrett's esophagus or another gastrointestinal disorder: thepresence of goblet cells; the presence of cytological and architecturalabnormalities; the presence of cell stratification; the presence ofmultilayered epithelium; the maturation of the surface epithelium; thedegree of budding, irregularity, branching, and atrophy in crypts; theproportion of low grade crypts to high grade crypts; the presence ofsplaying and duplication of the muscularis mucosa; the presence, numberand size of thin-walled blood vessels, lymphatic vessels, and nervefibers; the frequency of mitoses; the presence of atypical mitoses; thesize and chromicity of nuclei; the presence of nuclear stratification;the presence of pleomorphism; the nucleus:cytoplasm volume ratio; thepresence of villiform change; the presence of the squamocolumnarjunction (Z-line) and its location in relation to the gastroesophagealjunction; the presence of ultra-short segment Barrett's esophagus; theintestinal differentiation in nongoblet columnar epithelial cells; thepresence of longated, crowded, hyperchromatic, mucin-depleted epithelialcells; the degree of loss of cell polarity; the penetration of cellsthrough the original muscularis mucosa; the infiltration of dysplasticcells beyond the basement membrane into the lamina propria.

Conversion of Data into Scores

In some embodiments of the invention, the operator of the system,devices, apparatuses and compositions of the present invention are usedto identify one or more scores which can be correlated with clinicaldata from a subject to predict a clinical outcome, a clinical treatment,a responsiveness to a particular treatment, or a diagnosis of a subclassof a disease. In some embodiments, a subject or set of subjects isdiagnosed with a particular subclass of Barrett's esophagus. Afterpatterns are measured, a score or scores is assigned to the intensity orquantity of the identified patterns depending upon what descriptivefeatures are identified in one or more cell samples provided. In someembodiments, spatial distribution of biomarkers and their relation tocell samples taken from subject or subject identified as not havingBarrett's esophagus or other gastrointestinal disorder are reviewed todetermine a score. An algorithm is then used to compile each score orset of scores for each cell sample and output the likelihood that a cellsample taken from a subject having been indentified with agastrointestinal disorder may have a particular subclass of Barrett'sesophagus. In some embodiments, the method may comprises predictingwhether a subject identified as having Barrett's esophagus may haveBarrett's esophagus, no dysplasia, no progression in 5 years; Barrett'sesophagus, no dysplasia, progression to low/high grade dysplasia in 5years; Barrett's esophagus, indefinite for dysplasia, no progression in5 years; Barrett's esophagus, indefinite for dysplasia, progression tolow/high grade dysplasia or adenocarcinoma in 5 years; Barrett'sesophagus, reactive atypia; Barrett's esophagus, low grade dysplasia, noprogression in 5 years; Barrett's esophagus, low grade dysplasia,progression to high grade dysplasia or adenocarcinoma in 5 years;Barrett's esophagus, high grade dysplasia; or Esophageal adenocarcinomaarising in a background of Barrett's esophagus.

In one embodiment, the function used to correlate a score to aparticular diagnosis of a gastrointestinal disorder is based on apredictive model. In an embodiment, the predictive model is selectedfrom the group consisting of a partial least squares model, a logisticregression model, a linear regression model, a linear discriminantanalysis model, a ridge regression model, and a tree-based recursivepartitioning model. In an embodiment, the predictive model performanceis characterized by an area under the curve (AUC) ranging from 0.68 to0.70. In an embodiment, the predictive model performance ischaracterized by an AUC ranging from 0.70 to 0.79. In an embodiment, thepredictive model performance is characterized by an AUC ranging from0.80 to 0.89. In an embodiment, the predictive model performance ischaracterized by an AUC ranging from 0.90 to 0.99.

An example of a formula for a 4 feature classifier is:P _(progression)=1/e ^(−z)z=β ₀+χ₁β₁+χ₂β₂+χ₃β₃+χ₄β₄Where:

-   -   P_(progression)=probability of progression to low grade        dysplasia, high grade dysplasia or esophageal adenocarcinoma    -   χ=a feature    -   χ₁=0.99 quantile of p53 cell mean intensity    -   χ₂=0.99 quantile of HIF1alpha cell mean intensity    -   χ₃=0.05 quantile of beta-catenin cell mean intensity    -   χ₄=0.5 quantile of COX-2 plasma membrane:nucleus ratio    -   β=regression coefficient for each biomarker feature obtained via        fitting a generalized linear model using a logit link function        Methods

The invention relates to the use of the system, devices, apparatuses,kits and compositions to perform one or more steps of all of the methodsdiscloses herein.

The invention relates to a method of determining a risk of progressionof Barrett's esophagus in a subject, comprising: a) detecting a subsetof biomarkers in a sample from the subject, wherein two or morebiomarkers in said subset are selected from the group consisting p53,HIF-1alpha, beta-catenin, and COX-2; and b) determining at least one ormore descriptive features listed in Table 4 or 5 associated with saidbiomarkers, wherein the presence, absence, location, ratio, or quantityof descriptive features determines a score, relative to a control,wherein the score correlates to the risk of progression of Barrett'sesophagus in the subject. In another embodiment, at least one or morebiomarkers selected from the group consisting of p16, Ki-67,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B, CD68, CD4, forkhead box P3, CD45, thrombospondin-1,C-myc, cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, and FasL. In another embodiment, at least one or morebiomarkers selected from the group consisting of AMACR, CD1a, CD45RO,CD68, CK-20, Ki-67, NF-κB, and p16. In another embodiment, the subjecthas an increased risk of progression to low grade dysplasia, high gradedysplasia or esophageal cancer. In another embodiment, the subject isdiagnosed with no dysplasia, reactive atypia, indefinite for dysplasia,low grade dysplasia, or high grade dysplasia. In another embodiment, themethod further comprises detecting the subset of biomarkers using probesthat specifically bind to each of said biomarkers. In anotherembodiment, at least 10, at least 20, at least 30, at least 40, at least50, or 60 descriptive features are determined from Table 4. In anotherembodiment, at least 10, at least 20, at least 30, at least 40, at least50, at least 60, at least 70, at least 80, or 89 descriptive featuresare determined from Table 5.

The invention also relates to a method of classifying Barrett'sesophagus in a subject, comprising: a) detecting a subset of biomarkersin a sample from the subject, wherein two or more biomarkers areselected from the group consisting of HIF-1alpha, p53, CD45RO, p16,AMACR, CK-20, CDX-2, HER2/neu, CD1a, COX-2, NF-κB, and a nucleic acidbiomarker; and b) determining at least one or more descriptive featureslisted in Table 6 associated with said biomarkers, wherein the presence,absence, location, ratio, or quantity of descriptive features determinesa score, relative to a control, wherein the score correlates to theclassification of Barrett's esophagus. In another embodiment, at leastone or more biomarkers selected from the group consisting of Ki-67,beta-catenin, matrix metalloproteinase 1, CD68, CD4, forkhead box P3,thrombospondin-1, C-myc, fibroblast activation protein alpha, cyclin D1,EGFR, Interleukin-6, PLAU plasminogen activator urokinase (uPA), Fas,and FasL. In another embodiment, the classification of Barrett'sesophagus comprises no dysplasia, reactive atypia, low grade dysplasia,and high grade dysplasia. In another embodiment, the method furthercomprises detecting the subset of biomarkers using probes thatspecifically bind to each of said biomarkers. In another embodiment, atleast 10, at least 20, at least 30, at least 40, at least 50, at least60, at least 70, or 71 descriptive features are determined from Table 6.

In another embodiment, the methods further comprise the samplecomprising a brushing, biopsy, or surgical resection of cells and/ortissue from the subject. In another embodiment, the methods furthercomprise descriptive features that are identified in subcellular and/ortissue compartments. In another embodiment, the methods further comprisedescriptive features that further comprise one or more morphometricmarkers selected from the group consisting of nuclear area, nuclearequivalent diameter, nuclear solidity, nuclear eccentricity, gland tostroma ratio, nuclear area to cytoplasmic area ratio, glandular nuclearsize, glandular nuclear size and intensity gradient, and nucleartexture. In another embodiment, the methods further comprise the samplethat is at room temperature or frozen. In another embodiment, themethods further comprise the sample that is freshly obtained, formalinfixed, alcohol fixed, or paraffin embedded. In another embodiment, themethods further comprise probes that are fluorescent and/or comprise afluorescent tag, preferably wherein each probe is labeled with adifferent fluorophore. In other embodiment, the methods further comprisethe subset of biomarkers that comprise at least 3 biomarkers and whereinthe 3 biomarkers are an epithelial biomarker, immune biomarker and/or astromal biomarker. In another embodiment, the methods further detect astem cell biomarker. In another embodiment, the methods further comprisethe detection of 2 or more, 3 or more, 4 or more, 5 or more, 8 or more,or 12 or more biomarkers simultaneously. In another embodiment, themethods further comprise that the subject is a human.

The invention relates to a method of quantifying one or more biomarkersin a cell sample comprising: providing a cell sample, contacting aplurality of probes and or stains with cell sample either serially orsimultaneously, and determining relative quantity of probes bound to aplurality of biomarkers using the system comprising: (a) a cell sample;(b) a plurality of probes and/or stains that bind to biomarkers of thecell sample; (c) one or more optical scanners that generates digitalimaging data about the presence, absence, location, quantity, and/orintensity of at least one probe or stain that binds a biomarker of thecell sample; (d) one or more data processors, each in operablecommunication with at least one optical scanner, that, eitherindividually or collectively:

(i) receives the digital image data from the optical scanner and,optionally, transmutes said digital imaging data into a digital imagingsignal; and (ii) analyzes the digital image data to identify, measure,or quantify one or more descriptive features from the plurality ofprobes and/or stains; and (iii) converts the one or more descriptivefeatures into a score, wherein (iii) optionally comprises integratingstored data about a subject or group of subjects to convert the one ormore descriptive features into a score; (e) one or more monitors, eachin operable communication with at least one data processor, thatcomprises a screen and that receives a component of the digital images,or, optionally, receives the digital imaging signal from the dataprocessor and projects a digitally addressable image onto its screen;and (f) one or more data storage units, each in operable communicationwith at least one processor.

The invention also relates to a method of diagnosing Barrett's esophaguscomprising: (a) providing a cell sample of tissue; (b) contacting aplurality of probes with cell sample; (c) identifying one or moredescriptive features; (d) determining one or more scores based upon thepresence, absence, or quantity of descriptive features; and (e)correlating the score to a subclass of Barrett's esophagus. In someembodiments one or more steps of the method is/are performed using anyone or more of the compositions, apparatuses, devices, kits or systemsdisclosed herein.

Cell samples are obtained from a biopsy (such as a punch biopsy), cutand fixed onto a slide or slides, and then each slide or slides isdigitally imaged and digitally analyzed by on or more of the methodsdescribed herein to identify the presence, absence, relative quantityand/or spatial distribution of biomarkers in the cell sample.

The invention also relates to a method of determining patientresponsiveness to a therapy for gastrointestinal tract disorderscomprising: (a) providing a plurality of cell samples; (b) contacting aplurality of probes with each cell sample; (c) identifying one or moredescriptive features of each cell sample; (d) determining one or morescores of each cell sample based upon the presence, absence, or quantityof descriptive features; and (e) predicting patient responsiveness to atherapy to treat or prevent a gastrointestinal disorder based upon thescore.

The invention also relates to a method of compiling a cellular systemsbiological profile of a subject r set of subjects comprising: (a)providing one or more cell samples from a set of subjects; (b)contacting a plurality of probes with the one or more cell samples; (c)identifying one or more descriptive features for each cell sample; (d)determining one or more scores for each cell sample based upon thepresence, absence, or quantity of descriptive features; and (e)compiling the scores for each subject.

A method of classifying gastrointestinal tract tissues, comprising:determining, testing, calculating, or assessing a biomarker expressionprofile of each cell sample; and classifying the cells in clustersdetermined by similarity of biomarker expression profile. In someembodiments, the method of classifying gastrointestinal tract tissuescomprises determining a biomarker expression profile by using a kitdescribed herein.

A method of determining, testing, calculating, or assessing patientresponsiveness to a therapy for gastrointestinal tract disorderscomprising:

-   (a) providing a plurality of a cell sample;-   (b) contacting a plurality of probes with the cell sample;-   (c) identifying one or more descriptive features;-   (d) determining one or more scores based upon the presence, absence,    or quantity of descriptive features; and-   (e) predicting patient responsiveness to a therapy to treat or    prevent a gastrointestinal disorder based upon the score.

The invention also relates to a method of monitoring differentiation,morphology, or tumor progression of subject comprising: providing two ormore cell samples from said subject; determining an expression profileof each of the cell samples; classifying the cell samples into clustersdetermined by similarity of biomarker expression profile; ordering theclusters by similarity of biomarker expression profile; and determininga time course of biomarker expression levels for each of the pluralityof biomarkers at different stages of differentiation, morphology, ortumor progression in the cell samples. In some embodiments, the methodof comprises determining an expression profile using a kit describedherein.

The invention also relates to a method for identifying differentiallyexpressed biomarkers, comprising: determining a biomarker expressionprofile of each of a set of cell samples at different differentiation,morphology, or tumor stages; classifying the cells in clustersdetermined by similarity of biomarker expression profile; ordering theclusters by similarity of biomarker expression profile; and determininga time course of biomarker levels for each of the plurality ofbiomarkers at different stages of differentiation, morphology, or tumorstages in the cell samples; and identifying differentially expressedbiomarkers. In some embodiments, the method of identifyingdifferentially expressed biomarkers comprises using a kit describedherein.

The invention also relates to a method of identifying a specific celltype within a cell sample that contains a plurality of cells comprising:determining a biomarker expression profile of a plurality of cells;classifying the plurality of cells in clusters determined by similarityof biomarker expression profile; and determining the nature and functionof the plurality of cells. In some embodiments, the method ofidentifying a specific cell type within a cell sample that contains aplurality of cells comprises using a kit described herein.

Also described herein is a method for predicting Barrett's esophagus ina subject, including: obtaining a cell sample from the subject, whereinthe sample includes a plurality of analytes; contacting the cell samplewith a probe and/or dye or a probe set; generating a plurality ofcomplexes between the probe and/or probe set and the plurality ofanalytes; detecting the presence, absence, quantity, or spatiallydistribution of the plurality of complexes to obtain a dataset ofdescriptive features associated with the cell sample, wherein the firstdataset includes quantitative expression data for at least one biomarkerset selected from the group consisting of the marker sets in term 1,term 2, term 3, and optionally term 4, and optionally term 5, andoptionally term 6, and optionally term 7; wherein terms 1 through terms7 any combination of one or more biomarkcrs selected from the following:p16, p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase (AMACR,P504S), matrix metalloproteinase 1, CD1a, NF-kappa-B p65,cyclo-oxygenase-2, CD68, CD4, forkhead box P3, CD45, thrombospondin-1,C-myc, cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL and HIF-1alpha; and determining a score from thedataset using an interpretation function, wherein the score ispredictive of Barrett's esophagus in the subject.

Kits

The invention also relates to a kit comprising (a) a set of probesincluding one or a plurality of probes for determining a datasetassociated with descriptive feature or features for at least onebiomarker from a cell sample obtained from the subject; and (b)instructions for using the one probe or plurality of probes to determineone or more descriptive features from the cell sample; and, optionally,(c) software stored in a computer-readable format (such as a hard drive,flash drive, CD, DVD, disk, diskette, etc.) to convert any one or moredescriptive features into a score. In some embodiments, the inventionrelates to a kit comprising (a) a set of probes including one or aplurality of probes for determining a dataset associated withdescriptive feature or features for at least one biomarker from a cellsample obtained from the subject; and (b) software stored in acomputer-readable format to convert any one or more descriptive featuresinto a score.

The invention also relates to a kit for the prognosis of a particularclinical outcome of Barrett's esophagus comprising: (a) a set of probesincluding one or a plurality of probes for determining a datasetassociated with descriptive feature or features for at least onebiomarker from a cell sample obtained from the subject; and (b)instructions for using the one probe or plurality of probes to determineone or more descriptive features from the cell sample, wherein theinstructions include instructions for determining a score from thedataset wherein the score is predictive of a particular clinical outcomeof Barrett's esophagus in the subject. In some embodiments, theinvention relates to a kit for the prognosis of a particular clinicaloutcome of Barrett's esophagus comprising: (a) a set of probes includingone or a plurality of probes for determining a dataset associated withdescriptive feature or features for at least one biomarker from a cellsample obtained from the subject and chosen from the following: p16,p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S),matrix metalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2,CD68, CD4, forkhead box P3, CD45, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, FasL, HIF-1alpha; epithelial cells,multilayered-epithelial cells, endothelial cells, peripheral mononuclearlymphocytes, T cells, B cells, natural killer cells, eosinophils, stemcells, mast cells, macrophages, dendritic cells, neutrophils,fibroblasts, goblet cells, dysplastic cells, non-goblet columnarepithelial cells, 9p21, 8q24.12-13, or centromeres; and (b) instructionsfor using the one probe or plurality of probes to determine one or moredescriptive features from the cell sample, wherein the instructionsinclude instructions for determining a score from the dataset whereinthe score is predictive of a particular clinical outcome of Barrett'sesophagus in the subject.

In some embodiments, the invention relates to a kit for the prognosis ofa particular clinical outcome of Barrett's esophagus comprising: (a) aset of probes including one or a plurality of probes for determining adataset associated with descriptive feature or features for at least onebiomarker from a cell sample obtained from the subject and chosen fromthe following: p16, p53, Ki-67, beta-catenin, alpha-methylacyl-CoAracemase (AMACR, P504S), matrix metalloproteinase 1, CD1a, NF-kappa-Bp65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3, CD45,thrombospondin-1, C-myc, cytokeratin-20, fibroblast activation proteinalpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAU plasminogenactivator urokinase (uPA), CDX2, Fas, FasL, HIF-1alpha; epithelialcells, multilayered-epithelial cells, endothelial cells, peripheralmononuclear lymphocytes, T cells, B cells, natural killer cells,eosinophils, stem cells, mast cells, macrophages, dendritic cells,neutrophils, fibroblasts, goblet cells, dysplastic cells, non-gobletcolumnar epithelial cells, 9p21, 8q24.12-13, 17q11.2-q12, orcentromeres; and (b) instructions for using the one probe or pluralityof probes to determine one or more descriptive features from the cellsample, wherein the instructions include instructions for determining ascore from the dataset wherein the score is predictive of a particularclinical outcome of Barrett's esophagus in the subject.

In another embodiment, the invention relates to a kit for determining arisk of progression of Barrett's esophagus in a subject comprising: a)one or more probes that is capable of detecting at least two or morebiomarkers from the group consisting of p53, HIF-1alpha, beta-catenin,and COX-2; and b) instructions for using the probes to determine one ormore descriptive features to generate a score from a cell and/or tissuesample of a subject. In another embodiment, the kit further comprisesprobes that are capable of detecting at least one or more biomarkersdetected are selected from the group consisting of p16, Ki-67,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B, CD68, CD4, forkhead box P3, CD45, thrombospondin-1,C-myc, cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinase(uPA), CDX2, Fas, and FasL. In another embodiment, the kit furthercomprises probes that are capable of detecting at least one or morebiomarkers selected from the group consisting of AMACR, CD1a, CD45RO,CD68, CK-20, Ki-67, NF-κB, and p16. In another embodiment, the score ispredictive of the clinical outcome of Barrett's esophagus in the subjectand/or diagnostic of the subclass of Barrett's esophagus in the subject.In another embodiment, the probes comprise antibody probes thatspecifically bind to said biomarkers. In another embodiment, the probesare fluorescent and/or comprise a fluorescent tag. In anotherembodiment, at least 10, at least 20, at least 30, at least 40, at least50, or 60 descriptive features are determined from Tables 4. In anotherembodiment, at least 10, at least 20, at least 30, at least 40, at least50, at least 60, at least 70, at least 80, or 89 descriptive featuresare determined from Table 5. In another embodiment, the score ispredictive of the clinical outcome of Barrett's esophagus in the subjectand/or diagnostic of the subclass of Barrett's esophagus in the subject.In another embodiment, the probes comprise antibody probes thatspecifically bind to said biomarkers. In another embodiment, the probesare fluorescent and/or comprise a fluorescent tag.

The invention also relates to a kit for the diagnosis of a particularsubclass of Barrett's esophagus comprising: (a) a set of probesincluding one or a plurality of probes for determining a datasetassociated with descriptive feature or features for at least onebiomarker from a cell sample obtained from the subject; and (b)instructions for using the one probe or plurality of probes to determineone or more descriptive features from the cell sample, wherein theinstructions include instructions for determining a score from thedataset wherein the score is predictive of a diagnosis of the subjectfor a subclass of Barrett's esophagus. In some embodiments, theinvention relates to a kit for the diagnosis of a particular subclass ofBarrett's esophagus comprising: (a) a set of probes including one or aplurality of probes for determining a dataset associated withdescriptive feature or features for at least one biomarker from a cellsample obtained from the subject and chosen from the following: p16,p53, Ki-67, beta-catenin, alpha-methylacyl-CoA racemase (AMACR, P504S),matrix metalloproteinase 1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2,CD68, CD4, forkhead box P3, CD45, thrombospondin-1, C-myc,cytokeratin-20, fibroblast activation protein alpha, cyclin D1,HER2/neu, EGFR, Interleukin-6, PLAU plasminogen activator urokinasc(uPA), CDX2, Fas, FasL, HIF-1alpha; epithelial cells,multilayered-epithelial cells, endothelial cells, peripheral mononuclearlymphocytes, T cells, B cells, natural killer cells, eosinophils, stemcells, mast cells, macrophages, dendritic cells, neutrophils,fibroblasts, goblet cells, dysplastic cells, non-goblet columnarepithelial cells, 9p21, 8q24.12-13, 17q11.2-q12, or centromeres; and (b)instructions for using the one probe or plurality of probes to determineone or more descriptive features from the cell sample, wherein theinstructions include instructions for determining a score from thedataset wherein the score is predictive of a diagnosis of the subjectfor a subclass of Barrett's esophagus. The invention comprises kits forthe diagnosis of a particular clinical outcome. In another embodiment,the score is predictive of the clinical outcome of Barrett's esophagusin the subject and/or diagnostic of the subclass of Barrett's esophagusin the subject. In another embodiment, the probes comprise antibodyprobes that specifically bind to said biomarkers. In another embodiment,the probes are fluorescent and/or comprise a fluorescent tag.

In another embodiment, the invention relates to a kit for classifyingBarrett's esophagus in a subject, comprising: a) one or more probes thatis capable of detecting at least two or more biomarkers from the groupconsisting of HIF-1alpha, p53, CD45RO, p16, AMACR, CK-20, CDX-2, HER2,CD1a, COX-2, NF-κB, Ki-67, CD-68, Beta-catenin, and nucleic acid; and b)instructions for using the probes to determine one or more descriptivefeatures to generate a score from a cell and/or tissue sample of asubject.

In another embodiment, the kit further comprises probes that are capableof detecting at least one or more biomarkers selected from the groupconsisting of Ki-67, beta-catenin, matrix metalloproteinase 1, CD68,CD4, forkhead box P3, thrombospondin-1, C-myc, fibroblast activationprotein alpha, cyclin D1, EGFR, Interleukin-6, PLAU plasminogenactivator urokinase (uPA), Fas, and FasL. In another embodiment, atleast 10, at least 20, at least 30, at least 40, at least 50, at least60, at least 70, or 71 descriptive features are determined from Table 6.In another embodiment, the score is predictive of the clinical outcomeof Barrett's esophagus in the subject and/or diagnostic of the subclassof Barrett's esophagus in the subject. In another embodiment, the probescomprise antibody probes that specifically bind to said biomarkers. Inanother embodiment, the probes are fluorescent and/or comprise afluorescent tag.

Also described herein is a kit for predicting the responsiveness to atherapy for treating or preventing Barrett's esophagus or agastrointestinal disorder in a subject, comprising: (a) a set of probesincluding a plurality of probes for determining a dataset from a cellsample obtained from the subject for at least two biomarkers selectedfrom the group consisting of p16, p53, Ki-67, beta-catenin,alpha-methylacyl-CoA racemase (AMACR, P504S), matrix metalloproteinase1, CD1a, NF-kappa-B p65, cyclo-oxygenase-2, CD68, CD4, forkhead box P3,CD45, thrombospondin-1, C-myc, cytokeratin-20, fibroblast activationprotein alpha, cyclin D1, HER2/neu, EGFR, Interleukin-6, PLAUplasminogen activator urokinase (uPA), CDX2, Fas, FasL and HIF-1alpha;and (b) instructions for using the plurality of probes to determine thedataset from the sample, wherein the instructions include instructionsfor determining a score from the dataset, wherein the score ispredictive of a subject's responsiveness a therapy.

All of the aforementioned kits may optionally comprise any probe, dye,or set of probes and/or dyes specific for an analyte that corresponds tothe presence, absence, quantity, or spatial distribution of the of oneor more of the following cell types: epithelial cells,multilayered-epithelial cells, endothelial cells, peripheral mononuclearlymphocytes, T cells, B cells, natural killer cells, eosinophils, stemcells, mast cells, macrophages, dendritic cells, neutrophils,fibroblasts, goblet cells, dysplastic cells, and non-goblet columnarepithelial cells. Biomarkers or analytes associated to each cell typeare known throughout the art.

All of the aforementioned kits may optionally comprise any probe, dye,or set of probes and/or dyes specific for a chromosomal feature thatcorresponds to the presence, absence, quantity, or spatial distributionof one or more of the following chromosomal features: 9p21, 8q24.12-13,17q11.2-q12, or centromeres.

All of the aforementioned kits may optionally comprise software storedin a computer-readable format (such as a hard drive, flash drive, CD,DVD, disk, diskette, etc.) to convert any one or more descriptivefeatures into a score.

Although the present invention has been described in connection withcertain specific embodiments for instructional purposes, the presentinvention is not limited thereto. For example, although embodiments ofthe analysis system and computer-implemented network structure have beendescribed above in relation to the computer-aided detection of certainbiomarkers or subcellular organelles, the analysis system and networkstructure can equally be applied to detecting and analyzing targetpatterns in digital imagery of other spatially positioned objects on adigital image. For example, the analysis system can be used to detectand analyze anatomical regions of subcellular compartments, as well asthe frequency of different spatially positioned regions of an image.When analyzing cell samples depicted in digital images captured fromphotographic microscopes, thematic classes can be assigned to pixellocations that represent probed or un-probed cellular structures orbiomaterials. Accordingly, various modifications, adaptations, andcombinations of various features of the described embodiments can bepracticed without departing from the scope of the invention as set forthin the claims. Any and all journal articles, patent applications, issuedpatents, or other cited references are incorporated by reference intheir entirety.

EXAMPLE 1

Development of Tests to Predict Risk for Esophageal Adenocarcinoma inPatients with Barrett's Esophagus.

Project Goal: Develop a diagnostic and prognostic test or tests forBarrett's Esophagus predicting risk of developing esophageal cancer.

Clinical Need for Test: More than 339,000 upper GI biopsies areperformed in the US annually, and risk stratification is difficult forclinicians. Approximately 50% of patients first diagnosed withEsophageal Cancer (13,000 new cases/year in the US) are negative fordysplasia on their previous endoscopy procedure, which means manypatients at risk for cancer are simply missed by current endoscopicsurveillance. Furthermore, many biopsy results for Barrett's arereported as indefinite, leading to uncertainty in risk for developingcancer.

Indicated Use for Test: Patients undergoing endoscopy suspected ofhaving Barrett's Esophagus, and for which biopsy material will beavailable for analysis. Actionable Result: Classifier will identifypatients at low, high, or intermediate risk for esophageal cancer.Physician responsible for care can determine if procedure such as RadioFrequency Ablation (RFA), Endoscopic Mucosal Resection (EMR), or othertreatment method should be applied.

Assay Optimization and Build Training Patient Cohort

The multiplexed fluorescence staining conditions that produce optimalsignal:noise and correct staining pattern for 14 protein biomarkers(Table 2) were determined. Image analysis algorithms were developed toi) identify individual biopsy sections on slides containing multiplebiopsies, ii) remove autofluorescence from digital images of esophagealtissue sections, iii) segment individual nuclei, cytoplasms and plasmamembranes in digital images of esophageal tissue sections, iv) segmentsurface epithelium and glands from stroma in digital images ofesophageal tissue sections and v) extract quantitative biomarkerfeatures from subcellular compartments (nuclei, cytoplasm, plasmamembrane) and tissue compartments (epithelium, glands, stroma). Exampleimages of biomarkers in esophageal tissues and image analysis masks areshown in Phase 2, below.

TABLE 2 Diagnostic-Prognostic Biomarker Panel Biomarker CategoryBiomarker Epithelial/ Cytokeratin-20 (CK-20) Tumor Biomarkers CDX-2 p53p16 Ki-67 Beta-catenin α-methylacyl coenzyme A racemase (AMACR) HER/neuImmune Biomarkers CD68 CD45RO CD1a Stromal/ HIF-1alpha InflammatoryNuclear factor kappa B p65 (NF-κB) Biomarkers Cyclooxygenase 2 (COX-2)Training Cohort

The training cohort analyzed so far is described in Table 3. Thetraining cohort is being expanded to include cases from approximately200 cases.

TABLE 3 Summary of Training Cohort Number Diagnostic of Number ofSubcategory Cases Prognostic Subcategory Cases Barrett's esophagus, 17No progression 7 no dysplasia Progression to LGD 8 Progression toHGD/EAC 2 Barrett's esophagus, 14 No progression 6 reactive atypiaProgression to LGD 4 Progression to HGD/EAC 4 Barrett's esophagus, 14 Noprogression 5 indefinite for dysplasia Progression to LGD 5 Progressionto HGD/EAC 4 Barrett's esophagus, 16 No progression 7 low gradedysplasia Progression to LGD 1 Progression to HGD/EAC 8 Barrett'sesophagus, 11 n/a 11 high grade dysplasia Esophageal 6 n/a 6Adenocarcinoma Total Number of Cases 78 No progression: patients who didnot progress from no dysplasia, reactive atypia or indefinite fordysplasia to low grade dysplasia (LGD), high grade dysplasia (HGD) oresophageal adenocarcinoma (EAC) Progression to LGD: patients whoprogressed from no dysplasia, reactive atypia or indefinite fordysplasia to low grade dysplasia and patients who had multiple diagnosesof low grade dysplasia Progression to HGD/EAC: patients who presentedwith high grade dysplasia and patients who progressed from no dysplasia,reactive atypia, indefinite for dysplasia or low grade dysplasia to highgrade dysplasia or esophageal adenocarcinomaTraining Study to Evaluate the Diagnostic and Prognostic Significance ofthe Test and to Develop Diagnostic and Prognostic Classifiers

The 14 protein biomarkers and morphology described in Table 2 have beenevaluated in the initial training cohort of 78 patients in Table 3.

Methods: Multiplexed Fluorescence Biomarker Labeling and Imaging inEsophageal Tissues

Glass slides were prepared with 5 micrometer thick sections offormalin-fixed, paraffin-embedded esophageal biopsies. Slides were bakedat 60° C. for 30 minutes to melt paraffin and immersed in Aqua DePar(Biocare Medical) for 10 minutes at 75° C. to remove paraffin fromtissue sections. Slides were then immersed in antigen retrieval buffer(1 mM EDTA 10 mM Tris 0.05% Tween 20, pH9) at 99° C. for 20 minutesfollowed by room temperature for 20 minutes. Slides were washed twicefor 5 minutes each wash in tris-buffered saline 0.025% Tween 20 at roomtemperature and then Image-iT FX signal enhancer (Invitrogen) wasapplied for 30 minutes at room temperature. The signal enhancer wasreplaced with blocking buffer and slides were incubated for 30 minutesat room temperature.

Blocking buffer was then replaced with a cocktail of 3 primary antibodycocktails as follows for each subpanel:

-   Subpanel 1: rabbit IgG anti-Ki-67, mouse IgG2a anti-cytokeratin-20,    mouse IgG1 anti-beta-catenin;-   Subpanel 2: rabbit IgG anti-AMACR, mouse IgG2a anti-p16, mouse IgG2b    p53;-   Subpanel 3: rabbit IgG anti-COX2, mouse IgG3 anti-CD68, mouse IgG1    anti-NFkB p65;-   Subpanel 4: rabbit IgG anti-HIF-1alpha, mouse IgG2a anti-CD45RO,    mouse IgG1 anti-CD1a;-   Subpanel 5: rabbit IgG anti-HER2, mouse IgG2a anti-cytokeratin-20,    mouse IgG1 anti-CDX-2.

Slides were incubated with the primary antibody cocktails for 1 hour atroom temperature.

Slides were then washed thrice for 4 minutes each wash in tris-bufferedsaline 0.025% Tween 20 and blocking buffer was re-applied. Blockingbuffer was replaced with a fluorophore-conjugated species-specific,isotype-specific secondary antibody cocktail for each subpanel asfollows: Subpanel 1: Alexa Fluor 488-goat anti-rabbit IgG, Alexa Fluor555-goat anti-mouse IgG2a, Alexa Fluor 647-goat anti-mouse IgG1;Subpanel 2: Alexa Fluor 488-goat anti-mouse IgG2a, Alexa Fluor 555-goatanti-rabbit IgG, Alexa Fluor 647-goat anti-mouse IgG2b; Subpanel 3:Alexa Fluor 488-goat anti-mouse IgG3, Alexa Fluor 555-goat anti-mouseIgG1, Alexa Fluor 647-goat anti-rabbit IgG; Subpanel 4: Alexa Fluor488-goat anti-rabbit IgG, Alexa Fluor 555-goat anti-mouse IgG2a, AlexaFluor 647-goat anti-mouse IgG1; Subpanel 5: Alexa Fluor 488-goatanti-rabbit IgG, Alexa Fluor 555-goat anti-mouse IgG2a, Alexa Fluor647-goat anti-mouse IgG1. Slides were incubated with the secondaryantibody cocktails for 1 hour at room temperature.

Slides were washed thrice for 4 minutes each wash in tris-bufferedsaline and then 10 mg/ml Hoechst 33342 (diluted in deionized water) wasapplied to the slides for 3 minutes followed by washing in deionizedwater for 3 minutes. Slides were then air-dried and mounted withcoverslips using Prolong Gold Antifade medium (Invitrogen). Additionalserial sections were also stained with Hematoxylin and Eosin usingstandard histology methods.

Fluorescently-stained slides were scanned at 20× magnification on aScanScope FL with a DAPI/FITC/TRITC/Cy5 quadband filter (AperioTechnologies, Vista, Calif.). Optimal exposure times were determined foreach biomarker panel and the same exposure settings for each biomarkerpanel were applied to all slide scans. Example digital images of eachfluorescent channel for biomarker subpanels 1-5 are shown in FIGS. 4-8.Hematoxylin and Eosin-stained slides were scanned at 20× on NanoZoomerDigital Pathology slide scanner (Hamamatsu Corporation, K.K., Japan).

Image Analysis to Extract Quantitative Biomarker Data

Image analysis was performed on whole slide digital images of Barrett'sesophagus biopsies using Matlab software to develop specific imageanalysis algorithms. These algorithms were developed by Cernostics. TheCernostics image processing workflow consists of the followingcomponents: image detection, image validation, low order image objectsegmentation, feature measurement, and high order image objectsegmentation. A screenshot of Cernostics' dashboard for imageprocessing, segmentation and data extraction is shown in FIG. 9. Imagedetection consists of an algorithm for automatic detection of tissuesections in the whole slide image. Each tissue section hasauto-fluorescence from erythrocytes removed by an automated detectionalgorithm. Each tissue section is then submitted to a nuclei detectionalgorithm, which is in turn used to estimate cell cytoplasm. A plasmamembrane mask is calculated for markers known to express in the plasmamembrane. Cell, nuclei, and plasma membrane image masks are then used tocalculate image object features which consist of morphological shapemeasurements, marker expressions in the different cell compartments, andratios of marker expressions in the different cell compartments. The x-ycoordinates of each image object feature is recorded to enable spatialanalyses of biomarker expression. For each tissue section higher ordermasks to identify gland, epithelium, stroma, and inflammation arecalculated. Patterns of marker expression are then localized to thesehigher order image objects. The image analysis calculates the meanintensity of each biomarker in each cell or cell compartment. The singlecell distribution is summarized for each patient case in the indicatedpercentiles. Comparison of quantiles between diagnostic classes and riskclasses is more sensitive than comparing means in detecting samples withover-expression or loss of expression of biomarkers in small numbers ofcells. Example image analysis masks are shown in FIG. 10.

In FIG. 10, an esophageal biopsy slide stained for Subpanel 1 (Hoechst,Ki-67-Alexa Fluor 488, CK-20-Alexa Fluor 555, Beta-catenin-Alexa Fluor647) was scanned at 20× magnification (A). Image analysis was applied toidentify and segment individual biopsies on the slide (B). Whole biopsyimages of the upper right biopsy are shown in the four fluorescencechannels C: Hoechst, D: Ki-67, E: CK-20, F: Beta-catenin. Image analysiswas used to identify and remove autofluorescence (G), apply a nuclearedge mask (H), nuclear area mask (I), cell mask (J), plasma membranemask (K) and gland and stroma masks (L).

Statistical Analyses: Prognostic Significance—Stratifying CasesAccording to Risk of Progressing to LGD, HGD or EAC

276 features (mean intensity in cells or cell compartments, ratios ofone biomarker intensity between two cell compartments, ratios of twobiomarkers between one or two cell compartments, nuclear size, shape andintensity) were screened one at a time by logistic regression to producea univariate ranking of features that are significantly differentbetween non-progressors and progressors. 60 features with a p-value≤0.05 in the comparison of No Progression cases versus Progression toHGD/EAC cases are summarized in Table 4. The statistically significantfeatures in Table 4 are derived from the following biomarkers: AMACR,Beta-catenin, CD1a, CD45RO, CD68, COX2, HIF1alpha, Ki-67, NF-κB, p16,p53. 89 features with a p-value≤0.05 in the comparison of No Progressioncases versus Progression to LGD and Progression to HGD/EAC case aresummarized in Table 5. The statistically significant features in Table 5are derived from the following biomarkers: AMACR, Beta-catenin, CD1a,CD45RO, CD68, CK-20, COX2, HIF1alpha, Ki-67, NF-κB, p16, p53.

The top 50 features were selected and entered into a stepwise logisticregression procedure. The best model was chosen using Akaike'sinformation criterion. The resulting linear predictor utilizes thefollowing features:

p53 cellular mean intensity 99th percentile

HIF-1alpha cellular mean intensity 99th percentile

Beta-catenin cell mean intensity 5th percentile

COX2 plasma membrane: nucleus ratio 50th percentile

The features represent tumor/epithelial, inflammation and angiogenesisprocesses in the Barrett's esophagus tissue system.

A Receiver Operating Characteristics (ROC) curve for the multivariatepredictor and box plots are shown in FIG. 11 with an example cutoff thatproduces 90.9% specificity and 88.2% sensitivity in stratifying the noprogression group and the progression to HGD/EAC group. In FIG. 11, thetop 50 features from a univariate ranking of features to discriminate“no progression” cases from “progression to HGD/EAC” cases were selectedand entered into a stepwise logistic regression procedure. The bestmodel was chosen using Akaike's information criterion. The ROC plot(left) shows the sensitivity and specificity as a function of prognosticthreshold. The larger circle and dotted line and the table insert showthe result of an example decision analysis optimizing the trade-offbetween false-positives and false-negatives. The cost ratio is 1 and theprevalence odds ratio is 1. The plot on the right shows box plots forthe linear predictor with the dotted line at an example cutoff thatproduces 90.9% specificity and 88.2% sensitivity. The no progressiongroup consists of patients who did not progress to any type of dysplasiaor cancer. The Progression to HGD/EAC group consists of Barrett'sesophagus with low grade dysplasia, no dysplasia, reactive atypia orindefinite for dysplasia who progressed to high grade dysplasia oresophageal adenocarcinoma.

ROC curves and box plots for the top two features by univariate ranking(p53 and HIF1-alpha:CD1a) are shown in FIG. 12. In FIG. 12, the ROCplots show the sensitivity and specificity for p53 (A) andHIF1-alpha:CD1a ratio (C) as a function of predictive threshold. Thelarger circle and dotted line and the table insert show the result of anexample decision analysis optimizing the trade-off betweenfalse-positives and false-negatives. The cost ratio is 1 and theprevalence odds ratio is 1. Plots B and D show box plots for the linearpredictors with the dotted line at an example cutoff that produces 93.9%specificity and 58.8% sensitivity for p53 and 85.3% specificity and82.4% sensitivity for HIF1-alpha:CD1a ratio. The no progression groupconsists of patients who did not progress to any type of dysplasia orcancer. The Progression to HGD/EAC group consists of Barrett's esophaguswith low grade dysplasia, no dysplasia, reactive atypia or indefinitefor dysplasia who progressed to high grade dysplasia or esophagealadenocarcinoma. P values after Bonferroni adjustment are 0.0128 for p53and 0.0017 for HIF1alpha-CD1a.

Diagnostic Significance—Stratifying Cases According toSub-Diagnosis/classification of Barrett's Esophagus

205 features (mean intensity in cells or cell compartments, ratios ofone biomarker intensity between two cell compartments, ratios of twobiomarkers between one or two cell compartments, nuclear size, shape andintensity) were screened one at a time by logistic regression to producea univariate ranking of features that are significantly differentbetween Barrett's esophagus cases with no dysplasia or reactive atypiaversus Barrett's esophagus cases with low grade dysplasia or high gradedysplasia. Table 6 summarizes the 71 features that had a p value of≤0.05 in this analysis. The statistically significant features describedin Table 6 are derived from the following biomarkers and morphometrics:nuclei area, nuclei equivalent diameter, nuclei solidity, nucleieccentricity, DNA (Hoechst) intensity, HIF1alpha, p53, CD45RO, p16,AMACR, CK-20, CDX-2, HER2, CD1a, COX-2, NF-κB.

Table 7 lists significant diagnostic and prognostic biomarker featuresand subcellular localizations.

TABLE 4 Univariate Ranking of P Values from Logistic Regression of NoProgression Cases versus Progression to HGD/EAC Cases. Pr(>|z|) (p valuefrom linear Pvalue_LR (deviance/ Feature Name (with regression CI-lowerCI-upper likelihood ratio p percentile) Estimate Std. Error z value(Wald test) Effect (for effect) (for effect) value) HIF1alphamembrane:CD1a 0.650 0.218 2.976 0.003 1.915 1.248 2.938 0.0000042nucleus ratio 0.99 p53 Nuclei mean intensity 0.014 0.005 2.859 0.0041.014 1.004 1.024 0.0000305 0.99 p53 Cytoplasm mean 0.014 0.005 2.4780.013 1.014 1.003 1.025 0.0001976 Intensity 0.99 p53 Cell mean intensity0.99 0.015 0.006 2.374 0.018 1.015 1.003 1.028 0.0003323 HIF1alphaNuclei mean 0.020 0.007 2.661 0.008 1.020 1.005 1.035 0.0007769intensity 0.99 p53 Nuclei mean Intensity 0.016 0.008 2.091 0.037 1.0161.001 1.032 0.0009684 0.95 CD1a Cell mean intensity −0.289 0.102 −2.8370.005 0.749 0.613 0.914 0.0011908 0.01 CD1a −0.233 0.081 −2.874 0.0040.792 0.676 0.929 0.0012823 Cytoplasm_meanIntensity 0.01 CD1a Cell meanintensity −0.207 0.078 −2.661 0.008 0.813 0.697 0.947 0.0013442 0.05 p530.349 0.142 2.465 0.014 1.418 1.074 1.872 0.0015026AMACR_Cell_PlasmaNucRatio 0.99 CD1a_Cytoplasm_meanIntensity −0.214 0.081−2.647 0.008 0.808 0.689 0.946 0.0015588 0.05 HIF1alpha cytoplasm:CD1a0.032 0.012 2.722 0.006 1.032 1.009 1.056 0.0018362 membrane ratio 0.99p53 membrane:AMACR 0.763 0.368 2.075 0.038 2.145 1.043 4.410 0.0020232nucleus ratio 0.95 CD1a Nuclei mean intensity −0.197 0.076 −2.597 0.0090.821 0.708 0.953 0.0036836 0.01 CD1a Nuclei mean intensity −0.147 0.058−2.539 0.011 0.864 0.771 0.967 0.0039722 0.05 HIF1alpha Cytoplasm mean0.022 0.009 2.432 0.015 1.022 1.004 1.040 0.0040797 intensity 0.99 p53Cytoplasm mean 0.015 0.009 1.614 0.107 1.015 0.997 1.033 0.0042746intensity 0.95 p53_Cell_meanIntensity.0.95 0.016 0.009 1.708 0.088 1.0160.998 1.034 0.0044423 p53 cytoplasm:AMACR 0.017 0.008 2.197 0.028 1.0171.002 1.033 0.0048921 membrane ratio 0.99 HIF1alpha Cell mean intensity0.021 0.008 2.455 0.014 1.021 1.004 1.038 0.0049838 0.99 CD68 Nucleimean intensity 0.021 0.010 2.167 0.030 1.021 1.002 1.041 0.0052668 0.99NFKB cytoplasm:CD68 −1.032 0.451 −2.289 0.022 0.356 0.147 0.8620.0063369 membrane ratio 0.01 CD1a cytoplasm:CD45RO −1.928 0.859 −2.2440.025 0.146 0.027 0.783 0.0071582 membrane 0.05 AMACR membrane:P16−68.695 93.221 −0.737 0.461 0.000 0.000 3.288E+49 0.0077902 nucleusratio 0.5 AMACR membrane:nucleus −35.233 47.327 −0.744 0.457 0.000 0.0009.645E+24 0.0082491 ratio 0.5 Beta-catenin Nuclei mean −0.167 0.074−2.265 0.023 0.846 0.732 0.978 0.0085289 intensity 0.05 HIF1alphamembrane:CD1a 0.637 0.304 2.096 0.036 1.891 1.042 3.431 0.0091023nucleus ratio 0.95 Beta-catenin Cell mean −0.269 0.121 −2.221 0.0260.765 0.603 0.969 0.0091937 intensity 0.01 CD45RO cytoplasm:HIF1alpha−0.766 0.342 −2.241 0.025 0.465 0.238 0.908 0.0094542 membrane ratio0.01 P16 Cell mean intensity 0.01 −0.099 0.045 −2.210 0.027 0.906 0.8300.989 0.0108643 CD1a cytoplasm:CD45RO −3.866 1.844 −2.097 0.036 0.0210.001 0.777 0.0114551 membrane ratio 0.01 Beta-catenin Cell mean −0.1710.079 −2.155 0.031 0.843 0.722 0.985 0.0115135 intensity 0.05 NFKBcytoplasm:CD68 −0.393 0.199 −1.973 0.048 0.675 0.457 0.997 0.0123236membrane ratio 0.05 p53 cytoplasm:AMACR 0.026 0.014 1.937 0.053 1.0271.000 1.055 0.0154614 membrane 0.95 CD68 cytoplasm mean 0.019 0.0092.100 0.036 1.019 1.001 1.037 0.0162049 intensity 0.99 p53cytoplasm:nuclear −1.159 0.559 −2.074 0.038 0.314 0.105 0.938 0.0163040membrane ratio 0.01 COX2 membrane:NFKB 0.765 0.338 2.263 0.024 2.1491.108 4.170 0.0179868 nucleus ratio 0.95 CD68 cell mean intensity 0.990.019 0.010 1.962 0.050 1.019 1.000 1.039 0.0185398 COX2membrane:nucleus 0.265 0.144 1.842 0.065 1.304 0.983 1.729 0.0210546ratio 0.99 COX2 membrane:nucleus 2.707 1.212 2.232 0.026 14.977 1.391161.214 0.0216124 ratio 0.5 Beta-catenin cytoplasm mean −0.140 0.071−1.977 0.048 0.869 0.756 0.999 0.0217620 intensity 0.05 COX2cytoplasm:membrane −1.467 0.815 −1.801 0.072 0.231 0.047 1.138 0.0233622ratio 0.01 CD68 cytoplasm:membrane −0.645 0.340 −1.900 0.057 0.524 0.2701.021 0.0244353 ratio 0.05 CD45RO 0.299 0.184 1.622 0.105 1.348 0.9401.935 0.0250856 membrane:HIF1alpha nucleus ratio 0.99 AMACR cell meanintensity −0.240 0.127 −1.887 0.059 0.787 0.613 1.009 0.0258754 0.01Beta-catenin nuclei mean −0.147 0.074 −1.980 0.048 0.863 0.747 0.9990.0307526 intensity 0.01 Ki67 nuclei mean intensity 0.009 0.004 2.0360.042 1.009 1.000 1.018 0.0321749 0.99 CD68 cytoplasm:membrane −1.3530.711 −1.903 0.057 0.258 0.064 1.041 0.0325397 ratio 0.01 Ki67 nuclearmembrane:Beta- −1106.110 134419.988 −0.008 0.993 0.000 0.000 Inf0.0333813 catenin nucleus ratio 0.5 Ki67 nuclear membrane:total−1609.207 172094.078 −0.009 0.993 0.000 0.000 Inf 0.0333813 nucleusratio 0.5 HIF1alpha cytoplasm:CD1a 0.026 0.013 2.003 0.045 1.027 1.0011.054 0.0343305 membrane ratio 0.95 COX2_Cell_CytoPlasmaRatio. −0.5960.368 −1.617 0.106 0.551 0.268 1.135 0.0351331 0.05 Beta-atenincytoplasm mean −0.132 0.070 −1.888 0.059 0.876 0.764 1.005 0.0410004intensity 0.01 p53 Cell mean intensity 0.01 −0.263 0.140 −1.879 0.0600.769 0.584 1.011 0.0426466 COX2 membrane:nucleus 0.889 0.459 1.9370.053 2.432 0.990 5.980 0.0430243 ratio 0.95 Ki67 nuclear membrane:total−0.834 0.461 −1.812 0.070 0.434 0.176 1.071 0.0459256 nucleus ratio 0.99COX2 membrane:NFKB 3.248 1.696 1.915 0.055 25.746 0.927 714.9150.0505738 nucleus ratio 0.5 COX2 Cytoplasm:membrane −0.054 0.029 −1.8720.061 0.947 0.895 1.003 0.0510762 ratio 0.5 P16 Cytoplasm:membrane 0.2220.131 1.687 0.092 1.248 0.965 1.615 0.0510880 ratio 0.05 p53 Cell meanintensity.0.05 −0.159 0.091 −1.750 0.080 0.853 0.714 1.019 0.0526574

TABLE 5 Univariate Ranking of P Values from Logistic Regression of NoProgression Cases versus Progression to LGD and Progression to HGD/EACCases. Pr(>|z|) (p value from Pvalue_LR Feature name (with linearregression (Wald CI-lower CI-upper (deviance/likelihood percentile)Estimate Std. Error z value test) Effect (for effect) (for effect) ratiop value) p16 Cytoplasm:Plasma 1.264 0.432 2.925 0.003 3.538 1.517 8.2520.0003597 membrane Ratio 0.01 p16 Cytoplasm:Plasma 0.371 0.143 2.5910.010 1.449 1.094 1.917 0.0006047 membrane Ratio 0.05 AMACRcytoplasm:p16 0.726 0.278 2.609 0.009 2.066 1.198 3.565 0.0006340 Plasmamembrane Ratio 0.05 AMACR 2.183 0.779 2.802 0.005 8.869 1.927 40.8260.0009991 cytoplasm:p16Plasma membrane Ratio 0.01 p53 Nuclei meanintensity 0.010 0.004 2.402 0.016 1.010 1.002 1.018 0.0011155 0.99 CD1acell mean intensity −0.160 0.057 −2.824 0.005 0.852 0.763 0.9520.0011196 0.05 CD1a Nuclei mean −0.129 0.045 −2.848 0.004 0.879 0.8040.961 0.0015972 intensity 0.05 COX2 plasma 0.798 0.279 2.863 0.004 2.2221.286 3.837 0.0019325 membrane:NFKB nucleus Ratio 0.95 CD1a Cell meanintensity −0.212 0.076 −2.805 0.005 0.809 0.698 0.938 0.0019355 0.01 p53plasma 0.310 0.130 2.384 0.017 1.364 1.057 1.760 0.0025299membrane:AMACR nucleus Ratio 0.99 COX2 cytoplasm:plasma −1.515 0.634−2.388 0.017 0.220 0.063 0.762 0.0026297 membrane Ratio 0.01 CD1aCytoplasm mean −0.148 0.056 −2.652 0.008 0.862 0.773 0.962 0.0027675intensity 0.05 p53 nuclear 0.735 0.339 2.170 0.030 2.085 1.074 4.0480.0029764 membrane:AMACR nucleus Ratio 0.95 p53 Cytoplasm mean 0.0090.004 2.135 0.033 1.009 1.001 1.018 0.0030656 intensity 0.99 p16 Plasma−1.746 0.665 −2.627 0.009 0.174 0.047 0.642 0.0032675 membrane:nucleusRatio 0.95 p53 Cell mean intensity 0.011 0.005 2.149 0.032 1.011 1.0011.020 0.0036121 0.99 p16 Cell mean intensity −0.052 0.020 −2.615 0.0090.949 0.913 0.987 0.0038135 0.05 COX2 Plasma 2.821 1.039 2.716 0.00716.798 2.194 128.626 0.0038420 membrane:nucleus Ratio 0.5 COX2cytoplasm:plasma −0.067 0.025 −2.690 0.007 0.935 0.891 0.982 0.0046218membrane Ratio 0.5 COX2 Plasma membrane:nucleus 1.000 0.379 2.637 0.0082.718 1.293 5.714 0.0049389 Ratio 0.95 p16 Cell mean intensity −0.0880.035 −2.481 0.013 0.916 0.855 0.982 0.0053106 0.01 p53 cytoplasm:AMACR0.016 0.007 2.255 0.024 1.016 1.002 1.031 0.0054591 membrane Ratio 0.99p16 Nuclei mean intensity −0.040 0.016 −2.514 0.012 0.961 0.932 0.9910.0058166 0.05 CK20 cytoplasm:Ki67 0.006 0.002 2.575 0.010 1.006 1.0011.010 0.0058939 nuclear membrane Ratio 0.99 CD1a Cytoplasm mean −0.1500.059 −2.565 0.010 0.861 0.767 0.965 0.0059660 intensity 0.01 Ki67Nuclei mean intensity 0.009 0.004 2.492 0.013 1.009 1.002 1.0160.0068895 0.99 COX2 cytoplasm:plasma −0.568 0.273 −2.081 0.037 0.5670.332 0.968 0.0073582 membrane Ratio 0.05 CK20 cytoplasm:Ki67 −1.0380.423 −2.456 0.014 0.354 0.155 0.811 0.0079558 nuclear membrane 0.01CD1a Nuclei mean −0.135 0.054 −2.493 0.013 0.873 0.785 0.971 0.0080586intensity 0.01 CD68 cytoplasm:COX2 −0.911 0.453 −2.008 0.045 0.402 0.1650.979 0.0083563 plasma membrane Ratio 0.01 CK20 plasma 0.089 0.037 2.4190.016 1.093 1.017 1.174 0.0083992 membrane:Ki67 nucleus Ratio 0.99 Ki67nuclear −0.929 0.385 −2.411 0.016 0.395 0.185 0.840 0.0086131membrane:nucleus Ratio 0.99 p16 plasma −0.361 0.157 −2.300 0.021 0.6970.513 0.948 0.0088832 membrane:p53 nucleus 0.95 CD68 cytoplasm:COX2−0.032 0.013 −2.457 0.014 0.968 0.944 0.993 0.0092092 plasma membraneRatio 0.5 NFKB cytoplasm:CD68 −0.672 0.282 −2.381 0.017 0.511 0.2940.888 0.0094753 plasma membrane Ratio 0.01 COX2 plasma 3.402 1.440 2.3630.018 30.027 1.787 504.662 0.0097278 membrane:NFKB nucleus 0.5 p53Nuclei mean intensity 0.011 0.006 1.720 0.085 1.011 0.998 1.0240.0107267 0.95 CD1a Nuclei mean −0.046 0.021 −2.228 0.026 0.955 0.9170.994 0.0109146 intensity 0.5 HIF1alpha cytoplasm:plasma 0.181 0.0852.125 0.034 1.199 1.014 1.417 0.0111103 membrane Ratio 0.05 CK20cytoplasm:Ki67 0.009 0.004 2.360 0.018 1.009 1.002 1.016 0.0112116nuclear membrane Ratio 0.95 CK20 plasma 0.170 0.075 2.263 0.024 1.1851.023 1.373 0.0125502 membrane:Ki67 nucleus Ratio 0.95 HIF1alpha plasma0.209 0.098 2.133 0.033 1.232 1.017 1.492 0.0146451 membrane:CD1anucleus Ratio 0.99 p16 Nuclei mean intensity −0.017 0.008 −2.231 0.0260.983 0.968 0.998 0.0147046 0.5 CD1a Cell mean intensity −0.047 0.022−2.129 0.033 0.954 0.914 0.996 0.0147551 0.5 p16 Cell mean intensity 0.5−0.019 0.009 −2.225 0.026 0.981 0.965 0.998 0.0148032 AMACR −8.796 4.671−1.883 0.060 0.000 0.000 1.432 0.0149746 membrane:nucleus Ratio 0.5 NFKBcytoplasm:plasma −0.131 0.095 −1.375 0.169 0.877 0.727 1.057 0.0154471membrane Ratio 0.5 p53 cytoplasm:AMACR 0.026 0.013 1.986 0.047 1.0261.000 1.052 0.0164280 plasma membrane Ratio 0.95 AMACR membrane:P16−16.069 8.741 −1.838 0.066 0.000 0.000 2.896 0.0174328 nucleus Ratio 0.5CD68 cytoplasm:COX2 −0.286 0.157 −1.822 0.068 0.752 0.553 1.0220.0175976 plasma membrane Ratio 0.05 CD1a Nuclei mean −0.023 0.012−2.038 0.042 0.977 0.955 0.999 0.0181563 intensity 0.95 p53 Cell meanintensity 0.012 0.008 1.597 0.110 1.012 0.997 1.028 0.0187632 0.95CD45RO cytoplasm:HIF1A 0.104 0.052 2.011 0.044 1.110 1.003 1.2290.0223427 plasma membrane Ratio 0.05 p53 Cytoplasm mean 0.011 0.0071.437 0.151 1.011 0.996 1.025 0.0228740 intensity 0.95 P16 Cytoplasmmean −0.017 0.008 −2.086 0.037 0.984 0.968 0.999 0.0231425 intensity 0.5NFKB cytoplasm:membrane −1.136 0.603 −1.885 0.059 0.321 0.099 1.0460.0236419 Ratio 0.05 AMACR Nuclei mean −0.083 0.040 −2.061 0.039 0.9210.851 0.996 0.0255629 intensity 0.05 CD1a Cytoplasm mean −0.040 0.021−1.969 0.049 0.960 0.923 1.000 0.0257324 intensity 0.5 CD68cytoplasm:COX2 −0.027 0.013 −2.083 0.037 0.973 0.949 0.998 0.0270777plasma membrane Ratio 0.95 COX2 plasma 0.255 0.139 1.833 0.067 1.2900.983 1.695 0.0282164 membrane:nucleus Ratio 0.99 Ki67 Cytoplasm:nuclear−0.850 0.415 −2.047 0.041 0.427 0.189 0.965 0.0290736 membrane Ratio0.01 AMACR 0.502 0.262 1.921 0.055 1.653 0.990 2.760 0.0302793Cytoplasm:membrane Ratio 0.01 p16 cytoplasm:p53 nuclear −0.224 0.134−1.676 0.094 0.799 0.615 1.039 0.0315419 membrane Ratio 0.01 CD1a Nucleimean −0.015 0.008 −1.889 0.059 0.985 0.969 1.001 0.0320830 intensity0.99 CD1a Cell mean intensity −0.020 0.011 −1.844 0.065 0.980 0.9601.001 0.0366663 0.95 P16 plasma −5.337 3.154 −1.692 0.091 0.005 0.0002.325 0.0370970 membrane:nucleus Ratio 0.5 P16 plasma −1.667 1.106−1.508 0.132 0.189 0.022 1.649 0.0372701 membrane:p53 nucleus Ratio 0.5HIF1alpha 0.016 0.008 1.955 0.051 1.016 1.000 1.032 0.0389365cytoplasm:CD1a plasma membrane Ratio 0.99 AMACR Nuclei mean −0.013 0.007−1.902 0.057 0.987 0.974 1.000 0.0397682 intensity 0.99 HIF1alpha Nucleimean −0.024 0.013 −1.948 0.051 0.976 0.952 1.000 0.0407365 intensity 0.5CD68 cytoplasm:plasma −0.405 0.214 −1.891 0.059 0.667 0.438 1.0150.0409237 membrane Ratio 0.05 NFKB membrane:CD68 0.690 0.354 1.950 0.0511.994 0.997 3.989 0.0410493 nucleus ratio 0.5 HIF1alpha 0.130 0.0671.921 0.055 1.138 0.997 1.299 0.0427105 cytoplasm:CD1a plasma membraneRatio 0.01 AMACR Nuclei mean −0.033 0.018 −1.853 0.064 0.967 0.934 1.0020.0439938 intensity 0.5 CD1a Cytoplasm:Plasma 0.237 0.126 1.891 0.0591.268 0.991 1.622 0.0452599 membrane Ratio 0.01 NFKB cytoplasm:CD68−0.189 0.103 −1.828 0.067 0.828 0.676 1.014 0.0457721 plasma membraneRatio 0.05 AMACR Cell mean −0.082 0.045 −1.842 0.065 0.921 0.844 1.0050.0459226 intensity 0.05 CD1a Cytoplasm mean −0.019 0.010 −1.769 0.0770.982 0.962 1.002 0.0459685 intensity 0.95 CD68 Cytoplasm:Plasma −0.9210.491 −1.874 0.061 0.398 0.152 1.043 0.0459773 membrane Ratio 0.01 CD68Nuclei_mean 0.010 0.007 1.594 0.111 1.010 0.998 1.024 0.0484793intensity 0.99 p16 plasma:p53 nucleus −0.128 0.068 −1.872 0.061 0.8800.770 1.006 0.0488473 Ratio 0.99 P16 Nuclei mean intensity −0.006 0.004−1.818 0.069 0.994 0.987 1.001 0.0495530 0.99 AMACR Cell mean −0.0350.019 −1.803 0.071 0.966 0.930 1.003 0.0498546 intensity 0.5 NFKBcytoplasm:plasma −1.498 0.847 −1.767 0.077 0.224 0.042 1.177 0.0505213membrane Ratio 0.01 NFKB cytoplasm:plasma −0.014 0.008 −1.800 0.0720.986 0.972 1.001 0.0535724 membrane Ratio 0.95 CD1a Cell mean intensity−0.012 0.007 −1.720 0.085 0.988 0.975 1.002 0.0540265 0.99 Ki67 nuclear−1.618 0.892 −1.814 0.070 0.198 0.034 1.140 0.0543659 membrane:NucleusRatio 0.95 beta-catenin plasma 0.249 0.138 1.803 0.071 1.283 0.979 1.6810.0557799 membrane:CK20 nucleus Ratio 0.95 HIF1alpha Cell mean −0.0240.013 −1.800 0.072 0.976 0.951 1.002 0.0587989 intensity 0.5

TABLE 6 Univariate Ranking of Features by P Values from LogisticRegression of Barrett's esophagus no dysplasia and Barrett's esophagusreactive atypia cases versus Barrett's esophagus low grade dysplasia andBarrett's esophagus high grade dysplasia cases. Feature p value Nucleiarea 0.000242 Nuclei area 0.000261 Nuclei equivalent diameter 0.000302Nuclei equivalent diameter 0.000353 Hoechst cell mean intensity 0.000436Nuclei area 0.000736 HIF1 alpha cytoplasm:membrane ratio 0.001293Hoechst cell mean intensity 0.001588 Hoechst cell quantile intensity0.001975 Hoechst nuclei mean intensity 0.002379 p53 cytoplasm:nuclearmembrane intensity 0.002448 HIF1alpha cytoplasm:CD45RO plasma membraneintensity 0.002557 Hoechst cell mean intensity 0.002619 Nucleiequivalent diameter 0.002762 Nuclei area 0.003403 p16 cytoplasm:plasmamembrane ratio 0.003594 Hoechst cell quantile intensity 0.003991 Nucleiarea 0.00409 Hoechst nuclei mean intensity 0.004273 AMACR cytoplasm:p53nuclear membrane ratio 0.004531 Nuclei equivalent diameter 0.004562Hoechst cell quantile intensity 0.005236 CD45RO cytoplasm:plasmamembrane ratio 0.005248 CK-20 plasma membrane:CDX-2 nucleus ratio0.006393 CDX-2 nuclear membrane:nucleus ratio 0.006651 Hoechst nucleiquantile intensity 0.006928 Hoechst nuclei quantile intensity 0.008597Nuclei equivalent diameter 0.0092 Hoechst nuclei mean intensity 0.009652AMACR cytoplasm mean intensity 0.00988 CDX-2 cytoplasm:HER2 plasmamembrane ratio 0.009952 AMACR cell mean intensity 0.01121 CDX-2 cellmean intensity 0.011466 AMACR nuclear mean intensity 0.013133 Hoechstnuclei quantile intensity 0.014573 CDX-2 cytoplasm mean intensity0.016699 HER2 plasma membrane:CK-20 nuclear ratio 0.017772 Nucleisolidity 0.018268 Nuclei solidity 0.018759 CD1a cytoplasm:plasmamembrane ratio 0.019828 HER2 cytoplasm:CK-20 plasma membrane ratio0.020837 HER2 cytoplasm:plasma membrane ratio 0.021122 p53 cell quantileintensity 0.023041 p53 nuclei quantile intensity 0.023621 HER2 nucleimean intensity 0.024433 p16 cytoplasm:AMACR membrane ratio 0.024943 p53cell mean intensity 0.025204 CDX-2 nuclear mean intensity 0.025682 HER2cytoplasm mean intensity 0.025711 CD45RO cytoplasm:CD1a plasma membraneratio 0.026059 CK-20 cytoplasm:plasma membrane ratio 0.026579 COX-2cytoplasm:plasma membrane ratio 0.027833 CDX-2 cell quantile intensity0.028296 p53 cytoplasm:p16 membrane ratio 0.028584 HER2 nuclei quantileintensity 0.028831 p53 nuclei mean intensity 0.029849 HER2 cell meanintensity 0.030829 p53 cytoplasm mean intensity 0.031419 CDX-2 nucleiquantile intensity 0.032277 AMACR nuclear quantile intensity 0.034435HER2 cell quantile intensity 0.040346 p53 nuclear membrane:p16 nuclearratio 0.042366 p16 plasma membrane:nucleus intensity ratio 0.043742NF-κB cytoplasm:COX-2 plasma membrane ratio 0.044321 NF-κBcytoplasm:COX-2 nucleus ratio 0.045511 AMACR cytoplasm:membrane ratio0.046059 Hoechst cell mean intensity 0.048906 HIF1alpha cell meanintensity 0.050217 HIF1alpha cytoplasm mean intensity 0.051545 CD45ROcell mean intensity 0.05373 Nuclei eccentricity 0.058986

TABLE 7 Significant Diagnostic and Prognostic Biomarker Features andSubcellular Localizations Subcellular Biomarker Significant FeaturesLocalizations Tissue Localizations Cytokeratin-20 Mean intensity Plasmamembrane Glands, surface (CK-20) Quantile intensity Cytoplasmepithelium, tumor Ratio CK-20:Ki-67 Whole cell Ratio CK-20:beta-cateninRatio CK-20:CDX-2 Ratio CK-20:HER2 Ratio Cytoplasm:plasma membrane CDX-2Mean intensity Nucleus Glands, surface Quantile intensity Nuclearmembrane epithelium, tumor Ratio CDX-2:HER2 Whole cell Ratio CDX-2:CK-20Ratio Nuclear membrane:nucleus p53 Mean intensity Nucleus Glands,surface Quantile intensity Nuclear membrane epithelium, tumor Ratiop53:AMACR Whole cell Ratio Cytoplasm:nuclear membrane p16 Mean intensityCytoplasm Glands, surface Ratio Cytoplasm:membrane Plasma membraneepithelium, tumor, Ratio P16:AMACR Whole cell stroma Ki-67 Meanintensity Nucleus Glands, surface Ratio Ki-67:CK-20 Nuclear membraneepithelium, tumor, Ratio Ki-67:Beta-catenin Whole cell stroma Rationuclear membrane:total nucleus Beta-catenin Mean intensity Plasmamembrane Glands, surface Ratio Beta-catenin:Ki-67 Cytoplasm epithelium,tumor Ratio Beta-catenin:CK-20 Nucleus Ratio plasma membrane:cytoplasmWhole cell Ratio cytoplasm:nucleus Ratio plasma membrane:nucleusα-methylacyl Mean intensity Cytoplasm Glands, surface coenzyme AQuantile intensity Mitochondria epithelium, tumor, racemase Ratiocytoplasm:membrane Peroxisomes stroma (AMACR) Ratio membrane:nucleusNucleus Ratio AMACR:p53 Plasma membrane Ratio AMACR:p16 Whole cellHER/neu Mean intensity Plasma membrane Glands, surface Quantileintensity Cytoplasm epithelium, tumor Ratio cytoplasm:plasma membraneWhole cell Ratio HER2:CDX-2 Ratio HER2:CK-20 CD68 Mean intensity Plasmamembrane Stroma, glands, Ratio cytoplasm:plasma membrane Cytoplasmsurface epithelium, Ratio CD68:NF-κB Nuclei tumor Ratio CD68:COX-2 Wholecell CD45RO Mean intensity Plasma membrane Stroma, glands, RatioCD45RO:CD1a Cytoplasm surface epithelium, Ratio CD45RO:HIF1alpha Wholecell tumor Ratio cytoplasm:plasma membrane CD1a Mean intensity Plasmamembrane Stroma, glands, Ratio cytoplasm:plasma membrane Cytoplasmsurface epithelium, Ratio CD1a:HIF-1alpha Whole cell tumor RatioCD1a:CD45RO H1F-1alpha Mean intensity Nucleus Stroma, glands, RatioHIF1alpha:CD1a Cytoplasm surface epithelium, Ratio HIF1alpha:CD45ROPlasma membrane tumor Ratio cytoplasm:membrane Whole cell Nuclear factorMean intensity Cytoplasm Stroma, glands, kappa B p65 Ratiocytoplasm:plasma membrane Nucleus surface epithelium, (NF-κB) Ratioplasma membrane:nucleus Plasma membrane tumor Ratio NF-κB:COX-2 Wholecell Ratio NF:κB:CD68 Cyclooxygenase Mean intensity Plasma membraneStroma, glands, 2 (COX-2) Ratio plasma membrane:nucleus Nucleus surfaceepithelium, Ratio cytoplasm:plasma membrane Cytoplasm tumor RatioCOX-2:NF-κB Whole cell Ratio COX-2:CD68

The teachings of all patents, published applications and referencescited herein are incorporated by reference in their entirety.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A method of assigning the risk of progression ofBarrett's esophagus to a subject, comprising: a) obtaining a cell samplefrom the subject, wherein the cell sample contains cells or cellproducts from the upper gastrointestinal tract; b) labeling nuclei and aplurality of biomarkers using fluorescent probes, stains, or antibodiesin the cell sample from the subject, wherein the plurality of biomarkerscomprise KI67 (Ki-67), CD45RO, Cytokeratin-20 (CK-20), CDx2, p53, p16,HER2/neu, Alpha-methylacyl-CoA racemase (AMACR), Nuclear factor-kappa-B(NF-kB), Cyclo-oxygenase 2 (COX-2), CD68, CD1a, HIF-1α, andBeta-catenin; c) detecting the labeled nuclei and plurality ofbiomarkers with an optical scanner; d) generating digital image datafrom the detected nuclei and plurality of biomarkers; e) storing thegenerated digital image data in a computer-readable storage medium; f)analyzing the digital image data with a computer processor implementingcomputer-executable program code to produce pixel-based segmentation andobject-based classification of subcellular compartments and tissuecompartments; g) quantifying one or more descriptive features of eachbiomarker and nuclei, wherein the descriptive features are selected fromthe group consisting of morphometric markers selected from the groupconsisting of nuclear area, nuclear equivalent diameter, nuclearsolidity, nuclear eccentricity, gland to stroma ratio, nuclear area tocytoplasmic area ratio, glandular nuclear size, glandular nuclear sizeand intensity gradient and nuclear texture; and wherein the descriptivefeatures are further selected from the group consisting of localizationof a biomarker within the cell sample having a plurality of cells,spatial relationship between the location of biomarker and its positionin or among the cell sample or subcellular compartments within the cellsample, spatial distribution of one or more biomarkers, quantity and/orintensity of fluorescence of a bound probe, quantity and/or intensity ofa stain in the cell sample, presence or absence of morphologicalfeatures of cells within the plurality of cells, size or location ofmorphological features of cells within the plurality of cells, and copynumber of a probe bound to a biomarker of at least one cell from theplurality of cells; h) converting the descriptive features to a scoreusing a predictive statistical model developed in a set that comprisesdisease cases and unaffected controls, wherein the score is computed bycombination of descriptive features weighted by coefficients obtainedvia regression model, and the score is correlated to a risk ofprogression to dysplasia or esophageal adenocarcinoma; and i) using thescore to identify which subjects to treat; wherein the subject with ahigh risk score is treated using a clinical treatment selected from thegroup consisting of endoscopic surveillance, endoscopic eradicationtherapy, local or targeted therapy, systemic therapy, therapeuticintervention, and any combination thereof; and wherein the subject witha low risk score is not treated, avoiding unnecessary invasiveprocedures, and continuing endoscopic surveillance at a reducedfrequency or discontinuing endoscopic surveillance.
 2. The method ofclaim 1, wherein the labeled nuclei and plurality of biomarkers arelabeled with a probe.
 3. The method of claim 2, wherein the probe may bedirectly or indirectly conjugated to a fluorophore or histochemicalstain or dye, and wherein each probe is detected with a different stainor dye.
 4. The method of claim 1, wherein the endoscopic eradicationtherapy is single modality or multimodality and is performed alone or incombination with reconstructive therapies.
 5. The method of claim 1,wherein the descriptive features further comprise a clinical factorselected from the group consisting of age, gender, Barrett's segmentlength as a continuous or categorical variable, Barrett's segmentcircumference and maximal extent, presence/absence of hiatal hernia,pathologic/histologic diagnosis, body mass index, smoking status, andany combination thereof.
 6. The method of claim 1, wherein the pluralityof biomarkers are from stem cells, stromal cells, or a combinationthereof.
 7. The method of claim 1, wherein the cell sample from asubject is a brushing, a biopsy, or a surgical resection.
 8. The methodof claim 1, wherein the cell sample from the upper gastrointestinaltract is selected from the group consisting of a tissue specimen, abiopsy, a specimen consisting of bodily fluid, and any combinationthereof.
 9. The method of claim 8, wherein the cell sample contains acombination of two or more of the cell types selected from the groupconsisting of epithelial cells, multilayered-epithelial cells,endothelial cells, peripheral mononuclear lymphocytes, T cells, B cells,natural killer cells, eosinophils, mast cells, macrophages, dendriticcells, neutrophils, fibroblasts, goblet cells, dysplastic cells,non-goblet columnar epithelial cells, and any combination thereof. 10.The method of claim 1, wherein the therapeutic intervention isradiofrequency ablation, or mucosal resection.
 11. The method of claim1, wherein the plurality of biomarkers further comprises a biomarkerselected from the group consisting of Fas, FasL, Cyclin D1, C-MYC, EGFR,Nuclear factor-kappa-B p65 subunit (NF-kB p65), CD4, Forkhead box, P3(FOXP3), IL-6, uPA, Matrix metalloproteinase 1 (MMP1), Fibroblastactivation protein alpha (FAPα), Thrombospondin-1 (TSP1), 9p21,8q24.12-13, 17q11.2-q12, Chromosome enumeration probe 9, Chromosomeenumeration probe 8, Chromosome enumeration probe 17, and anycombination thereof.